Real World Derived Simulation Methodology for the Evaluation of Fleet Crash Protection of New Vehicle Designs

by Randa Radwan

B. S. in Electrical Engineering, May 1982, Rice University Masters of Electrical Engineering, May 1984, Rice University

A Dissertation submitted to

The Faculty of The School of Engineering and Applied Science of The George Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

May 17, 2015

Dissertation directed by

Azim Eskandarian Professor of Engineering and Applied Science

The School of Engineering and Applied Science of The George Washington University certifies that Randa Radwan has passed the Final Examination for the degree of Doctor of Philosophy as of February 6, 2015. This is the final and approved form of the dissertation.

Real World Derived Simulation Methodology for the Evaluation of Fleet Crash Protection of New Vehicle Designs

Randa Radwan

Dissertation Research Committee:

Azim Eskandarian, Professor of Engineering and Applied Science, Dissertation Director

Kennerly H. Digges, Research Professor of Engineering, Committee Member

Muhammad I. Haque, Professor of Engineering and Applied Science, Committee Member

Samer H. Hamdar, Assistant Professor of Civil Engineering, Committee Member

William T. Hollowell, President, WTH Consulting, Committee Member

iii Dedication

To My Sons Alexander and Peter,

To My Mom Whose Spirit Will Always be with Me,

To My Friends, Colleagues and Family,

Thank You for Your Boundless Encouragement, Support, & Faith in me!

iv Acknowledgments

The author wishes to thank Professor Azim Eskandarian for his work chairing the committee

and all of his effort as academic advisor, and Professors Kennerly Digges, Muhammad Haque,

and Samer Hamdar for their feedback and support as committee members. The author wishes to

thank Dr. Tom Hollowell for his work as committee member and his encouragement over the years. The author gratefully appreciates the financial support of the National Highway Traffic

Safety Administration (NHTSA) of the U.S. Department of Transportation (DOT) in the sponsored project under which most of the research was performed. The author wishes to thank

Dr. Cing-Dao Kan for his support and guidance during his tenure as director of the National

Crash Analysis Center (NCAC) of the School of Engineering and Applied Science.

The author wishes to thank her NCAC colleagues, NHTSA support staff, and NCAC consulting staff including Dr. Dhafer Marzougui, Dr. Chongzhen Cui, Ms. Lilly Nix, Ms. Aida

Barsan-Anelli, Dr. Fadi Tahan, Dr. Chung-Kyu Park, Dr. Pradeep Mohan, Dr. Tejas Ruparel, Mr.

Abdul Khan, Mr. Sarath Kamalakkannan, Mr. Vamsi Kommineni, and Mr. Stefano Dolci for performing extended vehicle and occupant model development and validations, and conducting hundreds of simulations whose output served as input to the methodology developed in this research. The author wishes to thank her sponsors at NHTSA including Mr. Stephen Summers and Ms. Lixin Zhao for their invaluable discussions and feedback on this research.

Finally, the author is immensely grateful for Dr. Priya Prasad’s guidance, mentorship, and insights throughout this research. The author also wishes to thank Professor Kennerly Digges for his support and collegiate inspiration throughout her tenure at the NCAC.

v Abstract of Dissertation

Real World Derived Simulation Methodology for the Evaluation of Fleet Crash Protection of New Vehicle Designs

At the present time, the crash safety of new and concept vehicle designs, i.e., with advanced materials or structure or powertrains, is primarily assessed through computer simulations of single- vehicle crash test protocols specified by existing regulations and consumer information programs.

Although such protocols are representative of real-world crash configurations, the tests are typically impacts into fixed object and are performed at single speeds with a single size of dummy occupants.

In the real world, vehicles are involved in crashes with fixed objects and with other vehicles in the fleet. These crashes occur at various speeds and involve occupants of many sizes and ages. To date, assessment of the real-world safety of vehicle designs has not been attempted through simulations because a systematic approach is not currently defined or developed. In this research, a novel methodology for Evaluating Fleet, i.e., self and partner, Protection (EFP) of new vehicle designs has been developed through a systems modeling approach driven by structural and occupant modeling, and real-world crash and full-scale test data. The fleet societal injury risk in EFP is defined as the total injury risk of occupants in both the target vehicle and partner vehicles aggregated over a range of impact speeds, occupant sizes, and crash configurations, and weighted by relative frequency of the specific crash incident in the real world. The self-protection provided by a target vehicle is derived as the aggregate injury risk for its occupants in both single- and two-vehicle crashes. Partner protection is derived as the aggregate injury risk for the occupants of the vehicles in the fleet against which the target vehicle collides. The integral feature of EFP is that the methodology is based on real-world crash configurations, severity exposures and occupant injury risks in each crash incident, and is also based on physically realistic vehicle structural, occupant, and restraint system models. The main hypothesis of EFP is as follows: Given that the approach is grounded in the physical world with sufficient detail (i.e., real-world derived with sufficient granularity), EFP will serve as a useful method vi to assess the overall safety of vehicle designs in the fleet and for directing future vehicle safety research efforts.

As proof-of-concept and initial application, EFP was implemented to assess real-world fleet societal risk in frontal crashes. The modeled frontal crash configurations and EFP weighting factors were derived from an innovative crash taxonomy based on real-world structural engagement from the

National Automotive Sampling System Crashworthiness Data System (NASS CDS). The EFP crash configuration weighting factors for two-vehicle crash simulations were modulated by real-world crash exposure by vehicle class. The weighting factors for the impact speeds of the simulated crash incidents were based on real-world distributions for the target vehicle class. Simulation data to drive the methodology were obtained from finite element vehicle structural models with sufficient physical detail that allow implementation of new designs such as lightweight materials, new powertrains, and new structural architectures. Occupant responses were based on three-dimensional articulated rigid body models of the occupant and the passenger compartment. Both the structural and the occupant models are subjected to validation and robustness checks for the modeled crash configurations.

Occupant injury potentials were derived from occupant responses using state-of-the-art biomechanical injury risk probability functions.

EFP was applied to compute the change in driver societal injury risk between baseline and concept light-weighted vehicle design variants. EFP was also applied to obtain insights of the safety interactions in frontal crashes in an assumed light-weighted fleet as compared with a baseline fleet, both consisting of two vehicle segments. Overall, there is a net decrease in safety on a fleet level and by vehicle segments modeled in the light-weighted fleets. While the proof-of-concept and initial implementation of EFP was to drivers in frontal crashes, the EFP methodology can be extended to all crash modes and occupants.

vii Table of Contents

Dedication ...... iv

Acknowledgments...... v

Abstract of Dissertation ...... vi

List of Figures ...... xiii

List of Tables ...... xx

Chapter 1. Introduction ...... 1

1.1. Fleet Crash Safety Performance Assessment: A Historical Perspective ...... 1

1.2. Changing U.S. Fleet ...... 2

1.3. State of the Art: Evaluation of Crash Safety of New Vehicle Designs ...... 3

1.4. Objective and Summary of Dissertation Research ...... 4

1.5. Contributions of this Work ...... 6

1.6. Roadmap of Dissertation Chapters ...... 9

Chapter 2. Literature Review ...... 11

2.1. Ford SSOM ...... 11

2.2. Volpe’s Systems Model...... 11

2.3. TNO’s Fleet Systems Model ...... 13

2.4. Laituri Accumulated Injury Risk ...... 14

2.5. Limitations of Previous Studies ...... 14

Chapter 3. Methods ...... 17

3.1. Research design: Formulation and Hypotheses ...... 17

3.1.1. Fleet Societal Risk ...... 18

3.1.2. Combined Injury Risk ...... 20

3.2. EFP Processes ...... 21 viii

3.2.1. Real World and Crash Test Databases Analysis Process ...... 22

3.2.2. Modeling and Analysis Process ...... 22

3.2.3. Safety Prediction Process ...... 23

3.3. Hypothesis/Assumptions ...... 24

3.3.1. Integral Feature of EFP ...... 24

3.3.2. Finite Element Models ...... 25

3.3.3. Occupant Response Models ...... 25

3.4. Crash Data Sources and Overview of Crash Environment...... 25

3.5. Fleet Vehicle Models for EFP Implementation to Frontal Crash Mode ...... 30

3.5.1. FE Models Source ...... 31

3.5.2. FE Model Readiness and Decoupling of Occupant Modeling...... 33

3.5.3. Vehicle FE Model Extended Validations ...... 33

3.6. NASS CDS Analyses: Methods and Research Design ...... 34

3.6.1. Field Crash Data Population ...... 34

3.6.2. NASS CDS Case Weights ...... 38

3.6.3. Summary of New Approach to Address Overly Influential

NASS Weights ...... 39

3.6.4. NASS CDS Case Weights Trimming: Data Analysis and Results ...... 40

3.6.5. Frontal Impact Taxonomy: Basis for EFP crash configurations ...... 51

3.7. Field Data Analyses: Crash Configurations and Body Regions ...... 55

3.7.1. Overall Sample and Weighted Data Populations ...... 55

3.7.2. Frontal Impact Crash Configurations ...... 57

3.7.3. Crash Involvement by Direction of Force (DOF) ...... 59

3.7.4. Serious Injuries by Body Region and Driver Age Group ...... 60 ix

3.8. Frontal Crash Population for EFP Implementation ...... 62

3.9. Research Design of EFP Simulation Matrices ...... 66

3.10. NASS CDS Crash Configuration EFP Weighting Factors ...... 69

3.11. NASS GES EFP Weighting Factors for Vehicle Class Exposure ...... 70

3.12. NASS CDS Impact Speed EFP Weighting Factors ...... 72

3.13. Injury Risks Functions for EFP Implementation to Frontal Crashes...... 80

3.13.1. Combined Injury Risk in this Research versus NCAP ...... 82

3.13.2. Head Injury Risk Function...... 83

3.13.3. Neck Injury Risk Function...... 84

3.13.4. Chest Injury Risk Function ...... 85

3.13.5. KTH Injury Risk Function ...... 85

3.14. Occupant and Restraint Models for Fleet Modeling ...... 86

3.15. Framework for EFP Occupant Model Development ...... 88

3.16. Development of the EFP Partner and Target Occupant Model Environments ..... 90

3.16.1. Generic Occupant Restraint System Models ...... 92

3.16.2. Vehicle Interior Geometry and Dummy Positioning ...... 93

3.16.3. Airbag and Pretensioner Firing Time Strategies...... 96

3.16.4. Restraint System Fine Tuning ...... 96

3.17. Verification of Vehicle and Occupant Models for Fleet Simulations ...... 97

3.17.1. Vehicle Structure FEM Robustness Checks ...... 97

3.17.2. Vehicle and Occupant Additional Checks and Trends Analysis ...... 100

3.18. Summary of Modeling Process for EFP Frontal Impact Implementation ...... 105

Chapter 4. EFP Computations, Results, and Discussions ...... 107

4.1. Proof-of-Concept EFP Application ...... 108 x

4.2. Occupant Responses and Injury Risk Computation ...... 109

4.3. Societal Injury Risk Computation for a Target Vehicle ...... 115

4.4. EFP Proof-of-Concept Application Results ...... 122

4.4.1. Taurus Self-Protection Occupant Risk ...... 124

4.4.2. Taurus Partner Injury Risk in Vehicle-to-Vehicle Crashes ...... 128

4.4.3. Limitations: Based on Proof-of-Concept Implementation ...... 130

4.4.4. EFP Insight from Proof-of-Concept Application...... 132

4.5. Frontal EFP Application to Concept Lightweight Vehicle Designs ...... 133

4.6. Vehicle & Occupant Models EFP Application Notes ...... 134

4.7. Frontal EFP Application to Concept Lightweight Vehicle Designs: Results and Discussion ...... 136

4.7.1. SIR over Speed by Target Vehicle and Occupant Size ...... 139

4.7.2. Contribution to SIR by Body Region over BES ...... 143

4.7.3. Contribution to SIR by Body Region over BES ...... 145

4.8. Alternate Chest Injury Risk Function: Case Study ...... 153

4.8.1. Selection of Alternate Chest Injury Risk Function ...... 153

4.8.2. Comparison of Chest and Overall Societal Injury Risk ...... 154

4.8.3. Comparison of SIR Trends for Taurus & Accord Targets ...... 156

4.9. EFP Scenario Analysis: Fleet Composition Changes ...... 159

4.9.1. 1st Scenario: All SUVs are CUVs ...... 160

4.9.2. 2nd Scenario: All PCs are Heavy PCs ...... 161

4.9.3. 3rd Scenario: All PCs are Light PCs...... 162

4.10. Frontal EFP Application: Lightweight vs. Baseline Fleet Safety Analyses ...... 163

4.10.1. EFP Approach to Illustrate Baseline versus Lightweight Fleet xi

Safety Interactions ...... 163

4.10.2. Societal Risk in Lightweight Fleets vs. Baseline Fleet ...... 167

4.11. Baseline and Lightweight Concept Vehicles Comparisons ...... 168

4.11.1. Accord Targets Comparisons ...... 168

4.11.2. Findings Highlights- Accord Targets ...... 172

4.11.3. Venza Targets Comparisons ...... 173

4.11.4. Findings Highlights- Venza Targets ...... 178

4.11.5. Societal Risk Trends over BES for the Baseline and Lightweight Fleets.. 179

Chapter 5. Conclusions ...... 183

5.1. EFP Insights, Limitations, and Potential Refinements ...... 184

5.1.1. Safety Insights from Initial Application of Methodology ...... 184

5.1.2. Field Data Analyses Findings and Insights ...... 187

5.1.3. CAE Process Insights from Methodology ...... 188

5.1.4. Limitations of Current Implementation of EFP ...... 189

5.1.5. Potential Refinements of EFP Frontal Implementation ...... 190

5.2. Potential Applications of Current EFP ...... 191

5.3. Potential Expansions of EFP ...... 193

xii

List of Figures

Figure 3-1. EFP Overview ...... 22

Figure 3-2. Schematic Presentation for Societal Risk Computation in Frontal Crashes ...... 24

Figure 3-3. Areas of IPI in GES and FARS data ...... 27

Figure 3-4. Passenger Cars Crash Involvment by IPI: All Severity and Fatal Crashes ...... 28

Figure 3-5. Light Trucks Crash Involvement by IPI: All Severity and Fatal Crashes ...... 28

Figure 3-6. Two-Vehicle Crashes: All Severity and Fatal Crashes ...... 30

Figure 3-7. National Estimates of BAIS2+ injuries to older drivers based on originally assigned NASS weights ...... 38

Figure 3-8. NASS mean weights – distribution by BES for MAIS3+F case population ...... 41

Figure 3-9. NASS mean weights – distribution by BES for MAIS2 case population ...... 41

Figure 3-10. NASS mean weights – distribution by BES for MAIS1 case population

(Note: mean weight at BES=6 is 65,279) ...... 42

Figure 3-11. NASS mean weights – distribution by BES for MAIS0 case population ...... 42

Figure 3-12. BAIS3+ injuries to younger drivers based on originally assigned and trimmed

NASS weights to 99th percentile of mean ...... 46

Figure 3-13. BAIS3+ injuries to older drivers, based on originally assigned and trimmed

NASS weights (99th percentile of mean) ...... 46

Figure 3-14. BAIS2+ injuries to younger drivers based on originally assigned & trimmed

NASS weights (99th percentile of mean) ...... 47

Figure 3-15. BAIS2+ injuries to older drivers based on originally assigned and trimmed

NASS weights (99th percentile of mean) ...... 48

Figure 3-16. Mean NASS weights for MAIS3+F population: Originally assigned versus xiii

trimmed study NASS weights ...... 48

Figure 3-17. Crash distributions by crash mode and model year for older drivers

(*effect of overly influential NASS weights) ...... 49

Figure 3-18. Crash distributions by crash mode (*effect of overly influential NASS weights, 99th percentile and 95th percentile of mean) ...... 49

Figure 3-19. BAIS3+ injuries to younger drivers based on trimmed NASS weights (both 99th percentile and 95th percentile of mean) ...... 50

Figure 3-20. BAIS3+ injuries to older drivers, based on trimmed NASS weights (both 99th percentile and 95th percentile of mean) ...... 50

Figure 3-21. BAIS2+ injuries to younger drivers, based on trimmed NASS weights (both 99th percentile and 95th percentile of mean) ...... 51

Figure 3-22. BAIS2+ injuries to older drivers, based on trimmed NASS weights (both 99th percentile and 95th percentile of mean) ...... 51

Figure 3-23. Study Frontal Impact Taxonomy (FIT) Groups ...... 53

Figure 3-24. Region for side damage for FIT ...... 54

Figure 3-25. NASS CDS Direction of Force (DOF) ...... 59

Figure 3-26. Frontal Crash Involvement by DOF ...... 60

Figure 3-27. Serious Injury Frontal Crashes by DOF ...... 60

Figure 3-28. Serious Injuries by Body Region for Modern Vehicles (* designate small sample size) ...... 61

Figure 3-29. Cumulative Frontal Crash Involvement by Driver Height ...... 66

Figure 3-30. Cumulative BES Distributions of Full Engagement Frontal Crashes for PC

Targets, Young Drivers ...... 74

Figure 3-31. Cumulative BES Distributions of Offset Frontal Crashes for PC xiv

Targets, Young Drivers ...... 74

Figure 3-32. Cumulative BES Distributions of Full Engagement Frontal Crashes

for PC Targets, Young Drivers ...... 75

Figure 3-33. Older vs. Younger Drivers Full Engagement Two-Vehicle Frontal

Crash BES Distribution ...... 75

Figure 3-34. Older vs. Younger Drivers Offset Two-Vehicle Frontal Crash BES Distribution .... 76

Figure 3-35. Cumulative BES Distributions of Full Engagement Frontal Crashes

for PC Targets, All Drivers ...... 77

Figure 3-36. Cumulative BES Distributions of Offset Frontal Crashes

for PC Targets, All Drivers ...... 77

Figure 3-37. Cumulative BES Distributions of Between Rail Frontal Crashes

for PC Targets, All Drivers ...... 77

Figure 3-38. Decoupled Frontal Impact Occupant Simulations ...... 87

Figure 3-39. Future Implementation of EFP Occupant Simulations ...... 88

Figure 3-40. General EFP Framework for Occupant Model Development

for Fleet Simulations ...... 89

Figure 3-41. Occupant Modeling Framework for Frontal Fleet Crash Simulations ...... 91

Figure 3-42. Initial position of 50th percentile Male dummy for Simulation in Yaris full frontal model ...... 94

Figure 3-43. Initial position of 5th percentile Female Dummy for Simulation

in Yaris full frontal model ...... 94

Figure 3-44. Lower Extremity Positioning of the 50th Percentile Dummy

in the Taurus Occupant Model ...... 95

Figure 3-45. Addition of Foot Stop to Taurus Occupant Model ...... 95 xv

Figure 3-46. Typical Shoulder Load from Crash Test ...... 97

Figure 3-47. Pre- and post-crash images of Yaris to Silverado full engagement impact ...... 98

Figure 3-48. Compartment acceleration of Yaris in full frontal impact with Silverado ...... 99

Figure 3-49. Pre- and post-crash images of Yaris to Silverado 40% offset frontal impact ...... 99

Figure 3-50. Compartment acceleration of Yaris in 40% offset impact with Silverado ...... 100

Figure 3-51. Yaris compartment accelerations for NCAP frontal verification simulations ...... 101

Figure 3-52. Yaris compartment accelerations for ODB frontal verification simulations ...... 102

Figure 3-53. Yaris compartment accelerations in Centerline pole verification simulations ...... 102

Figure 3-54. Taurus Occupant Model Simulation verification and robustness trends ...... 103

Figure 3-55. Yaris Occupant Model Simulation verification and robustness trends ...... 104

Figure 3-56. Explorer Occupant Model Simulation verification and robustness trends ...... 104

Figure 3-57. Silverado Occupant Model Simulation verification and robustness trends ...... 105

Figure 4-1. CIR Computation for Each Simulated Crash Incident for a Target Vehicle ...... 112

Figure 4-2. Overview of Societal Injury Risk Computation for a Target Vehicle,

SV=Single-Vehicle and VTV=Two-Vehicle Crash Simulations ...... 115

Figure 4-3. Societal Injury Risk Computation in Single-Vehicles Crashes for a Target Vehicle ...... 118

Figure 4-4. Taurus Baseline Societal Injury Risk Computation ...... 122

Figure 4-5. Taurus Driver Combined Injury Risk CIR in Single-Vehicle Crashes ...... 124

Figure 4-6. Head Resultant Acceleration for 50th Percentile Dummy in Taurus_ST

Offset Frontal Crash ...... 125

Figure 4-7. Taurus Driver Combined Injury Risk for Two-Vehicle Full Engagement Crashes .. 126

Figure 4-8. Taurus Driver Combined Injury Risk without Femur for Two-Vehicle Full Engagement Crashes ...... 127 xvi

Figure 4-9. Taurus Driver Combined Injury Risk in Two-Vehicle Offset Frontal Crashes ...... 128

Figure 4-10. Combined Injury Risk CIR for PC Partner Vehicle Impacted

by Taurus Vehicles ...... 129

Figure 4-11. Combined Injury Risk CIR for LT Partner Vehicle Impacted

by Taurus Vehicles ...... 129

Figure 4-12. Example of Upward Knee Rotation Driven by High Intrusion

in Taurus Baseline Occupant (right) as compared with Taurus_ST Occupant (left)...... 132

Figure 4-13. Distribution of Societal Injury Risk over BES in VTV by Target Vehicle ...... 141

Figure 4-14. Contribution of 5th Percentile Female Driver to SIR in VTV by Target Vehicle .... 142

Figure 4-15. Contribution of Head Serious Injury to SIR over BES by Target Vehicle ...... 146

Figure 4-16. Contribution of Chest Serious Injury to SIR over BES by Target Vehicle ...... 146

Figure 4-17. Contribution of Femur Serious Injury to SIR over BES by Target Vehicle ...... 147

Figure 4-18. Head Injury SIR over BES by Target Vehicle ...... 148

Figure 4-19. Contribution of 5th Percentile Female Driver to Head SIR over BES

by Target Vehicle ...... 148

Figure 4-20. Chest Injury SIR over BES by Target Vehicle ...... 149

Figure 4-21. Contribution of 5th Percentile Female Driver to Chest SIR over BES

by Target Vehicle ...... 149

Figure 4-22. Femur Injury SIR over BES by Target Vehicle ...... 150

Figure 4-23. Contribution of 5th Percentile Female Driver to Femur SIR over BES

by Target Vehicle ...... 151

Figure 4-24. Neck Injury SIR over BES by Target Vehicle ...... 152

Figure 4-25. Contribution of 5th Percentile Female Driver to Neck SIR over BES by Target Vehicle ...... 152 xvii

Figure 4-26. EFP Chest Injury Risk Functions for Hybrid III Midsize Male and Small Female Dummies ...... 154

Figure 4-27. Distribution of SIR over BES in VTV by Target Vehicle- Alternate

Chest Injury Risk ...... 157

Figure 4-28. Contribution of Head Injury to SIR- 2004 Prasad Chest Injury Risk

Function (left) versus NCAP (right) ...... 157

Figure 4-29. Contribution of Chest Injury to SIR- 2004 Prasad Chest Injury Risk

Function (left) versus NCAP (right) ...... 158

Figure 4-30. Contribution of Femur Injury to SIR- 2004 Prasad Chest Injury Risk

Function (left) versus NCAP (right) ...... 158

Figure 4-31. Contribution of 5th Percentile Female Driver to SIR- 2004 Prasad Chest

Injury Risk Function (left) versus NCAP (right) ...... 159

Figure 4-32. Accord Baseline versus Lightweight Compartment Pulse Comparisons at 25 and 35 mph ...... 170

Figure 4-33. VPI for the Accord Targets over Impact Speed ...... 171

Figure 4-34. Accord Target Maximum Dynamic Intrusions in IIHS Offset Deformable

Barrier (ODB) Frontal Impacts at the Foot well Area ...... 171

Figure 4-35. Accord Absorbed Energies for Vehicle Substructures Computed at end of FE Simulations ...... 172

Figure 4-36. Venza Baseline versus Lightweight Compartment Pulse Comparisons at 25 and 35 mph ...... 176

Figure 4-37. VPI for the Venza Targets over Impact Speed ...... 176

Figure 4-38. Venza Target Maximum Dynamic Intrusions in IIHS Offset Deformable

Barrier (ODB) Frontal Impacts at the Foot well Area ...... 177 xviii

Figure 4-39. Venza Target Maximum Dynamic Intrusions in IIHS Offset Deformable

Barrier (ODB) Frontal Impacts at the Knee Bolster ...... 177

Figure 4-40. Venza Absorbed Energies for Vehicle Substructures Computed at end

of FE Simulations ...... 178

Figure 4-41. SIR over BES for Baseline and Lightweight Fleets...... 179

Figure 4-42. Contribution of Small Female Driver over BES to SIR for Baseline

and Lightweight Fleets ...... 180

Figure 4-43. Contribution of Head Injury to SIR in Baseline and Lightweight Fleets...... 181

Figure 4-44. Contribution of Chest Injury to SIR in Baseline and Lightweight Fleets ...... 181

Figure 4-45. Contribution of Femur Injury to SIR in Baseline and Lightweight Fleets...... 182

xix

List of Tables

Table 3-1. Vehicles Overall and Fatal Crash Involvement by Body Type (2012 FARS

& NASS GES) ...... 27

Table 3-2. Selected FE Models for Representing Partner Vehicles (Initial status) ...... 32

Table 3-3. Candidate NASS CDS MAIS 3+F cases for weight reassignments...... 43

Table 3-4. Descriptive statistics of NASS weights for MAIS3+F case population ...... 44

Table 3-5. Descriptive statistics of NASS weights for MAIS2 case population ...... 44

Table 3-6. Descriptive statistics of NASS weights for MAIS1 case population ...... 44

Table 3-7. Descriptive statistics of NASS weights for MAIS0 case population ...... 44

Table 3-8. Cases identified as miscoded and removed from analysis ...... 45

Table 3-9. FIT with Small Overlap Side Groups: SideFront00-SideFront36 for Younger Driver and MY2000-2011 vehicles, (99th percentile Trimmed NASS Weights) ...... 55

Table 3-10. Younger Driver Sample Population: MY 1985+ Airbag Equipped Vehicles,

1995-2011 NASS CDS ...... 56

Table 3-11. Older Driver Sample Population: MY 1985+ Airbag Equipped Vehicles,

1995-2011 NASS CDS ...... 56

Table 3-12. Younger Driver Weighted Population: MY 1985+ Airbag Equipped Vehicles,

1995-2011 NASS CDS ...... 56

Table 3-13. Older Driver Weighted Population: MY 1985+ Airbag Equipped Vehicles,

1995-2011 NASS CDS ...... 57

Table 3-14. FIT Configurations: Serious Injury Distribution and Rates for Younger Driver,

MY 85+ airbag equipped vehicles ...... 57

Table 3-15. FIT Configurations: Serious Injury Distribution and Rates for Older Driver, xx

MY 85+ airbag equipped vehicles ...... 57

Table 3-16. FIT Configurations: Serious Injury Distribution and Rates for Younger Driver,

MY 2000+ airbag equipped vehicles ...... 58

Table 3-17. FIT Configurations: Serious Injury Distribution and Rates for Older Driver,

MY 2000+ airbag equipped vehicles ...... 58

Table 3-18. Serious Injury Rates by Body Region ...... 61

Table 3-19. Frontal Crash Population by Driver Age and Crash Event ...... 63

Table 3-20. Frontal Crashes MY 1985+ Airbag Equipped Vehicle ...... 63

Table 3-21. Younger Driver, FIT by Crash Events, MY 1985+ Vehicles, BES ≤ 40 mph,

EFP Fleet Segments ...... 64

Table 3-22. Younger Driver, FIT by Crash Events, MY 2000+ Vehicles, BES ≤ 40 mph,

EFP Fleet Segments ...... 65

Table 3-23. Cumulative Frontal Crashes by EFP Occupant Height Grouping ...... 66

Table 3-24. Single-Vehicle Crash Simulations ...... 67

Table 3-25. Two-Vehicle Frontal Crash Simulations ...... 68

Table 3-26. FIT by Crash Events, MY 1985+ Vehicles, BES ≤ 40 mph, EFP Fleet Segments

(No Airbag filter) ...... 69

Table 3-27. FIT by Crash Events, EFP Fleet Segments (No Airbag filter), with Between

Rail folded into Single-Vehicle Crashes ...... 70

Table 3-28. 2010 NASS/GES Vehicle Class Crash Exposure ...... 71

Table 3-29. 2012 NASS/GES Vehicle Class Crash Exposure ...... 71

Table 3-30. Crash Partner Pairings ...... 71

Table 3-31. Computed BES for Taurus Target in EFP Single-Vehicle Crash Simulations ...... 73

Table 3-32. Computed BES for Taurus Target in EFP Two-Vehicle Crash Simulations ...... 73 xxi

Table 3-33. EFP Impact Speed Weighting for PC Targets: Two-Vehicle Crashes ...... 78

Table 3-34. EFP Impact Speed Weighting for PC Targets: Single-Vehicle Crashes ...... 79

Table 3-35. EFP Impact Speed Weighting for CUV Targets: Two-Vehicle Crashes ...... 79

Table 3-36. Expanded Population Size for Two Vehicle Crashes for PC and LT Targets ...... 80

Table 3-37. Available Frontal Crash Tests for Partner Vehicles Occupant Model

Development ...... 92

Table 3-38. Post-crash Images of the Yaris for the Varying Speed Simulation

Trend Analysis ...... 101

Table 4-1. Material Properties of Steel in Taurus FE Models ...... 108

Table 4-2. Example Occupant Responses and Computed Injury Risks for 50th %tile Male Dummy ...... 110

Table 4-3. Example Occupant Responses and Computed Injury Risks for 5th %tile Female Dummy ...... 111

Table 4-4. Example Target Occupant Reponses and Computed Injury Risk Data in Single-Vehicle Crash Simulations ...... 113

Table 4-5. Example Target Occupant Reponses and Computed Injury Risk Data in Two-Vehicle Crash Simulations ...... 114

Table 4-6. CIR in Single-Vehicle Crash Incidents for the Taurus Vehicle Targets ...... 116

Table 4-7. CIR in Two-Vehicle Crash Incidents for the Taurus Vehicle Targets

(sum of target and partner driver injury risk) ...... 117

Table 4-8. Societal Risk in Single-Vehicle Crashes by Crash Configuration, crash Partner and Occupant Size for the Taurus Targets ...... 119

Table 4-9. Societal Risk in Two-Vehicle Crashes by Crash Configuration,

Light Truck Crash Partner and Occupant Size for the Taurus Targets ...... 119 xxii

Table 4-10. Societal Risk in Two-Vehicle Crashes by Crash Configuration,

PC Crash Partner and Occupant Size for the Taurus Targets ...... 119

Table 4-11. Accumulated Societal Risk in Single-Vehicle Crashes:

Overall & by Occupant Size ...... 120

Table 4-12. Frontal EFP Crash Configuration Weighting Factors ...... 120

Table 4-13. Accumulated Societal Risk in Two-Vehicle Crashes:

Overall & by Occupant Size ...... 120

Table 4-14. Weighting Factors for Crash Configuration modulated

by Vehicle Class Crash Exposure for Two-Vehicle Crash Simulation for PC Targets ...... 121

Table 4-15. Vehicle Crash Pairings – Normalized to 100% PC Target Class ...... 121

Table 4-16. EFP Computed Societal Risk for Taurus Targets ...... 122

Table 4-17. Toe Pan Intrusions and Femur Loads for Example Impacts

with Upward Knee Rotation ...... 132

Table 4-18. Reported FEM weights by Model Developer: Baseline and Design Variants ...... 135

Table 4-19. Baseline FE model weights ...... 135

Table 4-20. Single-Vehicle Crash Simulations ...... 136

Table 4-21. Two-Vehicle Frontal Crash Simulations ...... 137

Table 4-22. EFP Computed Societal Risk for Target and Lightweight Concept

Vehicle Designs ...... 137

Table 4-23. Field Serious Injury Rates for Younger Drivers ...... 138

Table 4-24. Field Serious Injury Rates for Older Drivers ...... 139

Table 4-25.Distribution of EFP Computed SIR for Target and Concept

Vehicle Designs over Speed ...... 140

Table 4-26. Contribution of Body Region: Head, Chest, Femur, and Neck to overall SIR ...... 143 xxii i

Table 4-27. Injury Sources for BAIS3+ Head Injuries in Frontal Crashes

MY 85+ airbag equipped vehicles, nearside belted driver AGE > 16,

NASS CDS National Estimates ...... 145

Table 4-28. Chest SIR by Target Vehicle: NCAP versus 2004 Prasad Risk Function...... 155

Table 4-29. Overall SIR by Target Vehicle: NCAP versus 2004 Prasad Risk Function ...... 155

Table 4-30. Field Serious Injury Rates for Younger Drivers ...... 156

Table 4-31. Baseline Fleet: Yaris, Taurus, Explorer, and Silverado ...... 160

Table 4-32. 1st Scenario Fleet: Yaris, Taurus, new Venza and Silverado ...... 160

Table 4-33. 2nd Scenario Fleet: Taurus, Explorer, and Silverado ...... 161

Table 4-34. 3rd Scenario Fleet: Yaris, Explorer, and Silverado ...... 162

Table 4-35. Distributions for Crash Pairs in Baseline, LW1, and LW2 Fleet Simulations ...... 164

Table 4-36. EFP Vehicle Class Weighting for Baseline, LW1, and LW2 Fleets ...... 165

Table 4-37. EFP Impact Speed Weighting for Accord Targets ...... 165

Table 4-38. EFP Impact Speed Weighting for the Venza Targets ...... 166

Table 4-39. Accord and Venza Baseline Vehicle Weights...... 167

Table 4-40. Fleet and Segment SIR: Lightweight versus Baseline Fleets ...... 167

Table 4-41. Accord Baseline Full Engagement Impact- Midsize Male Driver Responses ...... 169

Table 4-42. . Accord LW Full Engagement Impact- Midsize Male Driver Responses ...... 169

Table 4-43. Accord Baseline Full Engagement Impact- Small Female Driver Responses ...... 169

Table 4-44. Accord LW Full Engagement Impact- Small Female Driver Responses ...... 169

Table 4-45. Venza Baseline Full Engagement Impact- Midsize Male Driver Responses ...... 173

Table 4-46. Venza HO Full Engagement Impact- Midsize Male Driver Responses ...... 173

Table 4-47. Venza LO Full Engagement Impact- Midsize Male Driver Responses ...... 174

Table 4-48. Venza Baseline Full Engagement Impact- Small Female Driver Responses ...... 174 xxiv

Table 4-49. Venza HO Full Engagement Impact- Small Female Driver Responses ...... 174

Table 4-50. Venza LO Full Engagement Impact- Small Female Driver Responses ...... 174

xxv

List of Acronyms

AIS: Abbreviated Injury Scale

ATD: Anthropomorphic Test Device

BAIS: Body Region Abbreviated Injury Scale

BES: Barrier Equivalent Speed

BL: Baseline

CAFE: Corporate Average Fuel Economy

CARB: California Air Resources Board

CDS: Crashworthiness Data System

CIR: Combined Injury Risk

CUV: Crossover Utility Vehicle delta-V: Change in speed of a vehicle during a collision

DOF: Direction of Force

EPA: Environmental Protection Agency (EPA)

EFP: Evaluation of Fleet Protection

FARS: Fatality Analysis Reporting System

FE: Finite Element

FIT: Frontal Impact Taxonomy

FMVSS: Federal Motor Vehicle Safety Standards

GAD: General Area of Damage

GES: General Estimates System

HIC: Head Injury Criterion

IIHS: Insurance Institute for Highway Safety xxvi

KTH: Knee-Thigh-Hip

LT: Light Truck

LW: Lightweight

MADYMO: Mathematical Dynamic Modeling

MAIS: Maximum Abbreviated Injury Scale

MAIS 3+: Maximum Abbreviated Injury Scale of 3 or more, seriously injured

MY: Model Year

NASS: National Automotive Sampling System

NCAC: National Crash Analysis Center

NCAP: New Car Assessment Program

NHTSA: National Highway Traffic Safety Administration

Nij: Neck Injury Criterion

ODB: Offset Deformable Barrier

PC: Passenger Car

PSM: Prescribed Structural Motion

SIR: Societal Injury Risk (serious injury rate per 100 crash involved occupants)

SUV: Sport Utility Vehicle

VPI: Vehicle Pulse Index

VTV: Vehicle-to-Vehicle

xxvii

Chapter 1. Introduction

To date, the safety assessment of new vehicle designs is primarily established through single-

vehicle crash simulations using existing regulations and consumer information test protocols.

Real-world crashes involve wider-ranging impacts with fixed objects and other vehicles at

various speeds, with occupants of many sizes and ages. Assessment of real-world safety

performance of vehicle designs has not been attempted through simulations because a systematic

methodology is not available.

1.1. Fleet Crash Safety Performance Assessment: A Historical Perspective

Assessing crash safety performance of the vehicle fleet has long been recognized as an

integral study area of vehicle research by the safety community. In the mid-seventies, Ford Motor

Company developed the Safety Systems Optimization Methodology (SSOM), which was the

seminal research addressing fleet safety (Ford Motor Company 1978). SSOM used approximating

functions to model vehicle and occupant responses and was applied to identify structural

characteristics that maximized the safety performance of a mid-seventies 3000 lb. car in the

existing fleet. In the late nineties, Volpe National Transportation Systems Center developed a

fleet system approach with two vehicle types modeled by one-dimensional lumped mass models

to drive three-dimensional rigid body articulated occupant simulations. Volpe simulated barrier and two-vehicle frontal crashes and applied their approach to predict overall head and chest injuries weighted by the expected rate of occurrence in frontal impacts for single- and two-vehicle real-world crashes (Kuchar, Greif and Neat 2000) (A. Kuchar 2000). In the mid-2000s, TNO

Automotive of the Netherlands researched the potential of multi-body vehicle models for developing crashworthiness optimization strategies. TNO implemented three-dimensional

articulated rigid body structural models of several vehicle types constructed from existing finite

element models and included an integrated occupant (Kellendonk 2005) (Van Der Zweep 2005).

Crash simulations were performed for seven vehicles while varying impact speed and impact offset for a randomly generated distribution of the crash scenarios. TNO applied their models in design of experiments optimization studies for reducing overall injuries in vehicle-to-vehicle frontal crashes, by adjusting frontal stiffness of primary front-end load-carrying members, although geometric interaction was not considered.

1.2. Changing U.S. Fleet

The vehicle fleet in the United States (U.S.) is continually changing with the introduction of new vehicle architectures, new powertrains, and advanced restraints to meet new safety regulations and corporate fuel economy requirements, and to attain good ratings in consumer information test protocols. Vehicle architectures are also changing as more unitized structures for light trucks and vans are being introduced and additional load paths are being utilized for further improvements in compatibility between light and heavy vehicles, and in crash modes not considered in the past.

Advanced lightweight steels, plastics, and composites are being introduced into vehicle designs at a faster rate than in the past. In particular, innovative vehicle designs with advanced powertrains, e.g., electric vehicles and plug-in hybrids, and lightweight materials have been introduced in the U.S. fleet to improve fuel economy (Brooke and Evans 2009). In recent years, there has truly been a paradigm shift in the automotive industry towards a more fuel efficient vehicle fleet. Vehicle manufacturers are introducing innovative technologies, materials, and manufacturing processes, including new power trains and more mass reduction technologies, to meet Corporate Average Fuel Economy (CAFE) standards, stringent greenhouse gas (GHG)

standards, and increasing demand for more fuel efficient and cleaner vehicles. The composition

of vehicles in the US fleet is anticipated to change as a result of future CAFE Regulation

(NHTSA 2013) where the average mass of a vehicle is expected to get substantially lighter to meet higher fuel economy goals set by the Regulation.

Increasing demand for lighter vehicles is revealed in marketplace data, which show the

growth of the smaller car vehicle segments in the U.S. and the rest of the world. About 20% of all

new cars sold in the U.S. in 2012 were small cars (Market Data Center 2013). According to the

Wall Street Journal, sales of compacts and subcompacts increased substantially by almost 50% in

2012 (Read 2012).

1.3. State of the Art: Evaluation of Crash Safety of New Vehicle Designs

Traditionally, historical crash data are used to evaluate crash safety performance of vehicle

designs; however such data are limited for new and recent vehicle designs and are insufficient for

the identification of resulting fleet safety effects. Moreover, real-world crash safety implications

resulting from future changes in fleet segment composition and mass reduction are difficult to

estimate based on past experience. The limited historical crash data cannot be extrapolated into

the future because they may not apply to future designs with new materials and architecture.

The safety assessment of new and concept vehicle designs (with advanced materials,

structure, or powertrains) is currently established through computer simulations of single-vehicle

crash tests specified by existing regulations and consumer information protocols. The state of the

art in finite element (FE) modeling, encompassing both sophisticated software and powerful

computers, is advanced enough that detailed FE engineering models are developed first in the

pre-prototype stage of a new or concept vehicle and exercised in the various crash modes required

by existing regulations and consumer information testing programs (Deb 2004) (Babu 2012). FE

models have sufficient physical detail to allow implementation of the new designs such as

lightweight materials, new powertrains, and new structural architecture that cannot be performed

with lumped mass models. Examples include demonstrating the crashworthiness of a virtual

modular composite intensive concept vehicle (Fuchs 2006) and a 32% lightweighted design for a

crossover vehicle developed by Lotus Engineering (Lotus Engineering Inc. 2012). The regulatory

and consumer information crash tests are representative of real-world crash configurations but are

performed at single speeds with one or two sizes of occupants, and neither provides a broad

representation of all crash configurations nor captures vehicle-to-vehicle interactions. However,

the real world involves crashes at various impact velocities, configurations, impact partners (e.g.,

rigid obstacles, lighter or heavier vehicles), and vehicle occupants of various sizes and ages.

Moreover, the safety assessment of such designs is based on structural responses and not directly

on occupant responses since the vehicle interior and occupant environments are not currently

modeled in the concept vehicle design stage.

1.4. Objective and Summary of Dissertation Research

To date, determining the real-world interactions of new vehicle designs with both the

existing and future fleet has not been attempted through simulations because a systematic

approach has not been developed or defined. Similarly, the real-world crash safety implications resulting from future changes in fleet segment composition, such as the marketplace-driven growth in the smaller car segments in the U.S. or the introduction of vehicles designed to non-

U.S. safety requirements into the fleet, have not been determined through simulation because a

systematic approach has not been developed or defined.

The availability of FE engineering models for new and concept vehicles permits

simulation of a range of vehicle-to-vehicle (VTV) crash conditions. In this research, a

methodology that computes a measure of societal risk, i.e., both self- and partner-protection, from

various simulations of single-and two-vehicle crashes is developed and demonstrated. The

objective is to develop a method for the computation of fleet societal injury risk for a given target

vehicle of interest, e.g., a new concept or modified vehicle design. The methodology, referred to

as EFP for Evaluating Fleet, i.e., self and partner, Protection of new vehicle designs, is developed through a systems modeling approach driven by FE structural and rigid body occupant modeling and real-world crash and full-scale test data. The methodology consists of a virtual model simulating the real-world crash environment (i.e., different types of vehicles, impact velocities, impact directions, impact types, etc.) in which a concept or new vehicle design could be introduced and the safety of the occupants of such a vehicle and those of other vehicles involved in crashes with it would be evaluated.

The EFP methodology allows the assessment of self and partner safety of new vehicle

designs separately and in combination as a measure of overall fleet safety, i.e., societal injury

risk. The fleet societal injury risk is defined as the total injury risk of occupants in both the target

vehicle and partner vehicles aggregated over a range of impact speeds, occupant sizes, and crash

configurations, and weighted by relative frequency of the specific crash incident in the real world.

The integral feature of EFP is that the methodology is based on real-world crash configurations,

severity exposures, and occupant injury risks in each crash incident, and incorporates physically

realistic vehicle structural, occupant, and restraint system models. The main hypothesis of the

methodology is as follows: Given that the approach is grounded in the physical world with

sufficient detail (i.e., real world derived with sufficient granularity), the proposed EFP

methodology will serve as a useful tool to assess the overall safety of vehicle designs in the fleet

and for directing future vehicle safety research efforts.

An initial implementation of the EFP methodology to frontal crashes is demonstrated and

results from a proof-of-concept application to a baseline midsize passenger car and two simple design variants are presented. EFP is then applied to assess frontal crash safety performance of engineering models of concept lightweight vehicle designs developed in projects by the

California Air Resources Board (CARB), Environmental Protection Agency (EPA), and National

Highway Traffic Safety Administration (NHTSA) as part of the Corporate Average Fuel

Economy (CAFE) research efforts. EFP was also applied to obtain insights of the safety interactions in frontal crashes in an assumed lightweighted fleet as compared with a baseline fleet, both consisting of two vehicle segments.

1.5. Contributions of this Work

The new EFP methodology and novel analyses developed in this dissertation research offer the following unique additions to the state of the art in fleet safety assessment and crash environment characterization:

1. The frontal crash configurations for fleet model simulation are based on real-world crash

exposure and structural interactions from real-world distributions for both single- and

two-vehicle crashes in the National Automotive Sampling System (NASS)

Crashworthiness Data System (CDS). Frontal crashes were classified in a way that has

not been done before, by incorporating corner impacts. Small overlap crashes with side

damage were included as a subset of frontal corner crashes and overall taxonomy. The

expanded taxonomy provided a more comprehensive treatment of overall frontal crash

modes and permitted the assessment of relative crash involvement and contribution to

moderate and serious frontal crash injury of the Full Engagement, Offset, Between Rails,

and Corner frontal crash modes 6

2. A new and consistent method to identify and trim overly influential NASS CDS case

weights is developed and implemented. Highly influential CDS weights distort the

estimates of involvement and injury distributions and rates in crashes. The new method is

based on the statistics of the mean case weight by vehicle class and injury level, and is in

accordance with the NASS CDS sampling scheme at the case selection level. The

method is effectively applied in the analysis of the frontal crash population for the fleet

model.

3. Frontal crash exposure was established to be consistent across the age groups that were

modeled. The finding of increased fragility of the older population adds to the body of

knowledge by previous researchers.

4. The fleet is represented by four vehicles based on vehicle class and mass—light and

heavy passenger cars, and light and heavy Light Trucks [Sport Utility Vehicles (SUVs)

and Pickups]—to address anticipated changes in the vehicle fleet. This approach is

extensible and provides a more comprehensive inclusion of fleet representation than prior

fleet safety studies.

5. Unlike existing approaches which focus only on occupants of the target vehicle in single

vehicle configurations, the fleet societal risk is defined as the total safety of occupants in

both the target vehicle and crash partner vehicles. The societal injury risk is an aggregate

of individual crash injury risks weighted by real-world frequency of occurrence of a crash

incident summed over representative impact speeds, crash partners, crash configurations,

occupant sizes, and occupant seating locations.

6. Injury risk is based on a computation of a combined risk of injury to multiple body

regions, rather than separately for the head or chest as was used in existing fleet studies. 7

This provides a more comprehensive injury measure for the occupant, which accounts for

the important body regions in the societal risk.

7. As a component of EFP, a general framework for developing and validating vehicle and

occupant models for fleet simulations is established in two parts: first, model

development, followed by model verification and robustness simulations, both in the

crash configurations and impact speeds of interest. Finite element structural models

developed at the National Crash Analysis Center (NCAC) are used to represent the fleet

crash vehicles, which provide an accurate method of evaluating real-world interactions of

concept and new vehicle designs.

8. EFP identifies contribution to injury risk by occupant sizes: overall and over speed

ranges.

9. EFP identifies suitability of injury risk functions as compared with the real world

(NASS).

10. EFP can identify speed ranges, body regions, and occupant sizes for which a new design

(structure or powertrain or restraint system) is effective.

11. EFP can identify safety effects resulting from fleet composition changes.

With the aforementioned contributions, the EFP methodology, and the fleet model developed as an initial implementation to frontal crashes, can be potentially used by vehicles designers to ensure that a new vehicle design lowers the societal risk in the fleet. Policymakers can use such a fleet model to drive future vehicle safety and identify areas of future research.

1.6. Roadmap of Dissertation Chapters

The following outlines how the material in this dissertation is organized. Chapter 2 is a review of prior research relevant to vehicle fleet crash safety evaluation. Chapter 3 describes the formulation, hypotheses, and processes of the Evaluation of Fleet Protection (EFP) research presented in this dissertation, and establishes the solution approach and data sources for both the crash environment, and vehicle and occupant models. Chapter 3 also describes the data analyses for characterization of the crash mode, crash configurations, occupant size and serious injuries by body region for simulation. In addition, Chapter 3 presents the crash exposure and injury rate analyses for two occupant age groups and the selection of injury risks functions for the EFP implementation to frontal crashes.

Chapter 3 describes the research design of the EFP simulation matrices and development of

EFP real-world weighting factors from NASS CDS. Chapter 3 presents the approach and results highlights of the extended validation efforts for the selected vehicle structural models. Chapter 3 also presents the framework for EFP occupant model development and the highights for development of the partner and target occupant model environments. Lastly, Chapter 3 describes the verification of vehicle and occupant models for fleet simulations and presents a summary of modeling process for the implementation of EFP to frontal impacts.

Chapter 4 presents EFP computations, applications and results. Chapter 4 first presents the proof-of-concept application of EFP frontal impact implementation in which driver societal risk is assessed for three midsize target vehicles developed at the NCAC: a baseline and two simple design variants. The EFP injury risk computations and detailed results are methodically illustrated through the proof-of-concept application. Chapter 4 next presents an application of EFP to compute and assess the change in driver societal injury risk between three vehicles: a midsize passenger car and two light-weighted design concepts of a midsize CUV. These vehicle designs 9

were developed in recent projects as part of the Corporate Average Fuel Economy (CAFE) research efforts for NHTSA. Lastly, Chapter 4 presents a frontal EFP case study of a lightweight versus baseline fleet, each composed of two vehicle segments: a midsize passenger car and a midsize CUV. This case study provides insight into future light-weighted fleet safety interactions.

Within the scope of the applications, Chapter 4 demonstrates the EFP capability to assess suitability of injury risk functions in a chest injuries case study, and demonstrates the use of EFP to assess the safety effects of fleet composition changes in several “what-if” scenarios.

Chapter 5 presents overall conclusions and summarizes EFP research findings. Chapter 5

describes the safety insights from the initial application of the EFP methodology as well as the

limitations and potential refinements. Chapter 5 also describes potential applications and future

expansions of current EFP.

Chapter 2. Literature Review

2.1. Ford SSOM

The seminal research addressing fleet safety was performed by Ford Motor Company for the

National Highway Traffic Safety Administration (NHTSA) in the mid-seventies and resulted in

the Safety Systems Optimization Methodology (SSOM), which was further refined by the

University of Virginia (UVA) (Ford Motor Company 1978) (White, Jr., Pilkey and Sieveka

1986). SSOM made useful predictions regarding fleet crash safety using simple approximating functions (Summers and Hollowell 2001). In SSOM, the approximating functions were derived from crash pulses output by one-dimensional lumped mass model simulations of vehicle-to- vehicle crashes and from occupant motion as predicted by two-dimensional articulated lumped mass models. The output of the occupant response models, an acceleration time history, was converted into a fatality probability and combined with a cursory distribution of crashes to estimate overall injury rates by a weighted cost-based sum of injuries of different severities.

SSOM had the capability for direct-search, constrained optimization searches among the vehicle and restraint parameter combinations to minimize the overall number of fatalities and injuries for a given cost and vehicle weight constraints. A benchmark application of the SSOM simulation optimization approach to the design of a passenger vehicle, based on a 1975 Ford Pinto, for maximum safety performance in frontal crashes was performed subsequently by UVA (White, Jr., et al. 1985).

2.2. Volpe’s Systems Model

In the late nineties, additional development on the fleet system modeling approach for estimating total injuries in a range of crashes was performed at Volpe National Transportation

Systems Center in support of vehicle aggressivity studies (Kuchar, Greif and Neat 2000) (A.

Kuchar 2000). The Volpe systems model used one-dimensional lumped-mass full frontal models to simulate the vehicle responses for a passenger car and a sport utility vehicle (SUV) in barrier and vehicle-to-vehicle crashes. The vehicle models were extracted from full barrier crash tests and were simulated in full barrier and vehicle-to-vehicle full frontal impacts at seven speeds ranging from 22.5 km/h (14 mph) to 87 km/h (54 mph) with an emphasis on the higher speeds.

They assumed these crashworthiness models approximate angled and offset crashes. The passenger car fleet was represented by a vehicle model based on a 1995 Chevrolet Lumina, and the LTV fleet was represented by a model of the 1995 Ford Explorer. The Volpe systems model implemented MADYMO (MAthematical DYnamic MOdels)1 three-dimensional rigid-body articulated models for occupant simulations to predict head and chest injuries, which were driven by the occupant crash pulse from the one-dimensional lumped models and the toe board and knee bolster intrusions obtained from observations of crash test films. The Volpe systems model estimated total injuries as a sum of maximum head or chest harm, from different injury severity levels estimated by biomechanical risk functions at each simulated event (504 total) and weighted by the expected rate of occurrence in frontal impacts for single- and two-vehicle crashes in the

1992-1997 National Automotive Sampling System (NASS) Crashworthiness Data System (CDS).

NASS CDS data were first collected in 1979 and provide a nationally representative random sample of the entire U.S. passenger car crash population. The Volpe systems model was applied to investigate the trend in overall occupant injuries as a function of LTV/Car mix and a change in injury severity upon reducing the LTV front end stiffness in the lumped parameter model in a hypothetical future vehicle fleet subset.

1 MADYMO is a MAthematical DYnamic MOdeling software developed by the Netherlands TNO Automotive Safety Solutions division (TASS) for the analysis of occupant safety systems in the automotive and transport industries. 12

2.3. TNO’s Fleet Systems Model

The Netherlands Organization for Applied Scientific Research (TNO) Automotive performed

research for NHTSA, the European Commission, and the Dutch Ministry of Traffic to study the

potential use of multi-body vehicle models to develop strategies for crashworthiness optimization and to evaluate the effects of new vehicles on overall injury risk in car-to-car crashes, i.e., fleet-

wide (Kellendonk 2005) (Van Der Zweep 2005). In the mid- to late-nineties, finite element (FE)

structural models of high-sales vehicles were developed for the public domain for NHTSA

through a rigorous reverse engineering process (Marzougui, Kan and Bedewi 1996), (Zaouk, et

al. 1996), and (Kirkpatrick 1999). For the TNO fleet systems model, MADYMO vehicle

structural and occupant rigid body articulated models were constructed from the public domain

finite element models and validated to New Car Assessment Program (NCAP) data with main

structural load paths compared with measured static data (lower and upper rails, lower and upper

crossbeams). Seven MADYMO models, each representing a different class of vehicle, were

available: subcompact, compact, midsize, full size, and large passenger cars, a midsize SUV, and

two variants of the smallest vehicles with modified front-end geometry (e.g., stiffer longitudinal,

stronger interaction between upper and lower load-paths for one vehicle, and adding an entirely a

new sub-frame as a third load path for the other vehicle). These seven models were used to

represent the U.S. vehicle fleet. Crash simulations were performed across vehicle types while

varying impact speed (range of 25-75 km/h) and impact offset or overlap (range of 25-80%)

where a randomly generated even distribution of the scenarios (a total of 4200) were chosen for

the simulations based on a stochastic approach. TNO applied biomechanical injury risk functions

for the head and chest. Using statistical analysis tools, the simulation results were processed and

relevant trends were extracted from the large set of data. The fleet injury computed by TNO was

based on real-world occurrences of the simulated cases, which were statistically chosen based on 13

permutation of crash scenarios. TNO applied the rigid body models in design of experiments

(DOE) optimization studies to reduce overall injuries in vehicle-to-vehicle frontal crashes by adjusting frontal stiffness of the main load-carrying members in the front-ends, although geometric interaction was not considered.

2.4. Laituri Accumulated Injury Risk

Development of an accumulated injury risk metric for drivers in full engagement frontal crashes over a range of impact speeds and occupant sizes by body region, based on real-world frequency of occurrence from the NASS CDS, was introduced in 2003 (Laituri, Kachnowski, et al. 2003). Laituri et al. constructed a MADYMO model of an “average” car based on extensive crash test data and used NASS CDS crash event vehicle extent of damage and observed floor/toe- pan intrusions to classify the severity of frontal crashes as hard-contact versus soft-contact. The

MADYMO model was driven by vehicle crash pulses output from a one-dimensional lumped mass model.

2.5. Limitations of Previous Studies

While SSOM introduced the fleet safety concept for a single vehicle and used a combination of crash modes, SSOM used an approximation function to model the vehicle and occupant responses and pre-dated national accident files, biomechanical injury risk functions, abbreviated injury scale (AIS), nonlinear finite element codes, and the more developed articulated multi-body occupant models. While Volpe added the concept of multiple vehicle types and implemented biomechanical injury risk functions at multiple severity (i.e., AIS) levels for the fleet injury risk computation, they applied full frontal structural one-dimensional models with the assumption that these models are sufficient to simulate frontal offset and narrow object crashes, and their analysis of the crash environment did not account for vehicle class or structural engagement. Volpe 14

performed simulations of impact speeds up to 88 km/h, which is far beyond the range of 56 km/h

for which the one-dimensional models were developed and validated. Volpe also oversampled the

simulations at higher impact speeds based on the premise of higher occurrence of serious injuries

(AIS 3+), while in reality the majority of serious injuries occur in delta-Vs under 56 km/h. TNO simulated a range of vehicle overlap (25-80%) with potentially unrealistic predictions as the approximated rigid body vehicle models did not have the needed details to capture the dynamic structural responses at low overlap ranges. Also, the base FE models from which the TNO models were extracted were not verified in the frontal offset mode for any range of overlap. Similarly to

Volpe, the TNO models were simulated at 20-80 km/h impact speeds where the high end is an extrapolation beyond the intended performance range of the original FE models, which were not validated beyond 56 km/h (35mph). Also, TNO’s simulation matrix was not based on observed real-world crash frequencies, but was designed per a statistical approach based on permutation of crash scenarios for the seven vehicles modeled. That resulted in a small number of cases from the

NASS database for comparison; thus a detailed analysis of separate scenarios was not possible and all passenger cars were taken as one group and light trucks were not considered.

The EFP methodology developed in the current research computes an aggregate measure of societal risk, i.e., both self- and partner-protection, from various simulations of single- and two-

vehicle crashes based on a combined injury risk for multiple body regions. A concept vehicle

could be introduced in this virtual crash environment, and the safety of the occupants of such a

vehicle and those of other vehicles with which it collides could be evaluated. As noted in Chapter

1, EFP is a systems modeling approach driven by FE structural and rigid body occupant modeling

and real-world crash and full-scale test data. The methodology consists of a virtual model simulating the real-world crash environment (i.e., different types of vehicles, impact velocities,

impact directions, impact types, etc.) in which a concept or new vehicle design could be 15

introduced and the safety of the occupants of such a vehicle and those of other vehicles involved in crashes with it would be evaluated. As described in the following chapters, EFP advances the state of the art of systems modeling in crash safety simulation and addresses limitations of the previous system modeling efforts.

Chapter 3. Methods

In this Chapter, the goal, formulation, hypotheses, and processes of Evaluation of Fleet

Protection (EFP) research are presented. The solution approach and data sources for both the

crash environment, and vehicle and occupant models are established. The data analyses for characterization of the crash mode, crash configurations, occupant size and serious injuries by

body region for simulation are presented. The crash exposure and injury rate analyses for two occupant age groups and the selection of injury risks functions for the EFP implementation to frontal crashes are also described.

In this Chapter, the research design of the EFP simulation matrices and development of EFP real-world weighting factors from NASS CDS are presented. In addition, the extended validation

efforts for the vehicle structural models and the development studies for the occupant models that

were formulated for the EFP research at the National Crash Analysis Center (NCAC) of the

George Washington University (GWU) are outlined. The framework for EFP occupant model

development and the highights for the development of the partner and target occupant model

environments are presented. Lastly, in this Chapter, the verification of vehicle and occupant

models for fleet simulations is described and a summary of modeling process for the

implementation of EFP to frontal impacts is presented.

3.1. Research design: Formulation and Hypotheses

The goal of the EFP research is to develop a new Computer-Aided Engineering (CAE)

methodology to consistently evaluate real-world fleet crash safety using quantifiable measures

through vehicle structural and occupant modeling for current and new vehicle designs. The

objective is the computation of fleet societal injury risk for a given target vehicle of interest, e.g.,

a new concept or modified vehicle design.

3.1.1. Fleet Societal Risk

The fleet societal risk in the EFP methodology is defined as the total safety of occupants in

the target vehicle and crash partner vehicles (R. R. Samaha, P. Prasad and D. Marzougui, et al.

2013). This new safety performance measure is a metric for crash involved occupants and takes into account the safety of occupants in a target vehicle and the occupants of other vehicles with which it collides in the vehicle fleet across a range of impact speeds and crash configurations.

The governing equation to compute societal risk, as defined in this research, is as follows:

( ) = / ( ) 푁𝑁𝑁 퐽�퐽𝐽� 퐾𝐾𝐾𝐾 퐿𝐿𝐿�퐿� 푀�푀푀푀𝑀푀 푁푁𝑁푁�푁 푇 푃 푃푃�푃푃푃 푖=1 �=1 �=1 �=1 �=0 �=1 �=1 �=1 푖푖푖푖푖푖푖푖 푆푆푆 푣 ∑ ∑( ) ∑ ∑ ∑ ∑ ∑ ∑ 푤 푣 ∗ (3-1)

푖푖푖푖푖푖푖푖 The 퐶Societal𝐶 Injury푣 Risk (SIR) for a target vehicle is an aggregate of individual crash injury

risks weighted by the real-world frequency (풗 ) of a crash incident discretized over the

풊풊풊풊풊풊풊풊 following parameters and represents an injury풘 rate per 100풗 crash involved occupants:

• P impact speeds

• O target/partner vehicle

• N crash partners

• M crash configurations

• L occupant sizes

• K occupant seating locations

• J crash events (single vehicle, two/multiple vehicle)

• I crash modes

( ) represents a Combined Injury Risk (for multiple body regions) at a single

푖푖푖푖푖푖푖푖 crash퐶 𝐶incident for푣 a given occupant. A crash incident corresponds to a crash at a given impact speed for a given occupant size in a given seating position, in the target or partner vehicle, in a given crash configuration, in a single- or two-vehicle crash, and in a given crash mode.

( ) designates the weighting factor for the crash incident, i.e., percent frequency of

푖푖푖푖푖푖푖푖 occurrence푤 derived푣 from the National Automotive Sampling System Crashworthiness Data

System (NASS CDS).

The formulation of the governing equation for proposed societal risk is based on the following hypotheses:

• Different crash modes, i.e., frontal, side, rollover, and rear crashes, result in different

structural engagement, vehicle deformation, and occupant kinematics. Correspondingly,

occupants in the different crash modes are exposed to different crash loading

environments and body region injury distributions and mechanisms. As such, the societal

risk computation should be segregated by crash mode. In addition, different

configurations within a given crash mode, i.e., full engagement, offset, oblique, etc. will

be needed to simulate the various crash modes.

• To date, the safety community has been conducting single-vehicle regulatory and

consumer information crash tests to develop safety countermeasures and occupant

restraint systems in the different crash modes. As such, the societal risk computation

should be segregated by single- and two- (or more) vehicle crash events. This will allow

assessment of safety in the events for which the countermeasures were designed and of

how well that performance translates to the more predominant multiple-vehicle crash

events. 19

• To date, safety countermeasures and restraint systems have been primarily designed for

the average size male occupant. As such, the societal risk computation should be

segregated by occupant size to allow the assessment of safety performance for the

average male and of how effective that performance is for the wide population of

occupants in the real world.

• Real-world occupancy rates for different seating locations are taken into account by the

weighting factor.

3.1.2. Combined Injury Risk

In EFP, an injury risk function is desired that addresses the important body regions injured in

real-world crashes for a given crash mode. In this research, a combined serious-injury risk

function, CIR, is formulated for a given crash incident for the frontal crash mode as follows:

= (1 1 ( 3 +) 1 ( 3 +) 1 ( 3 +)

𝑜�표𝑜𝑜� ℎ𝑒� �ℎ𝑒� 𝑛𝑛 퐶�� (1 − � −(푃푃푃3푏+))) 퐴𝐴 � ∗ � − 푃푃푃푏 퐴𝐴 � ∗ � − 푃푃푃푏 퐴𝐴 (3� -∗2)

𝑓𝑓� The individual− 푃푃푃푏 probability퐴𝐴 risk of serious injuries to the head, neck, chest, and knee-thigh-hip

(KTH) complex is estimated by using state-of-art biomechanical injury risk functions. The injury risk functions are driven by the relevant responses output by the occupant modeling for the head, neck, check, and femur body regions. Serious injuries are designated by AIS3+, where the

Abbreviated Injury Scale (AIS), ranging between 1 (minor injury) to 6 (maximum severity injury), is used to code the severity of each individual injury to the head, face, neck, thorax, abdomen, spine, lower extremities, and upper extremities, based on the threat to life posed by the particular injury (AAAM 1990).

In the CIR calculation, the injuries to the different body regions are assumed independent.

Assuming the independence of occupant injury by body region has been an established practice in

automotive biomechanics research, although some dependence would be expected, especially in

higher severity crashes. Nevertheless, having a combined injury measure provides a

comprehensive injury measure for the occupant that accounts for the most frequently injured

body regions. In the government 2011 New Car Assessment Program (NCAP) frontal impact

testing, which provides consumers relative safety performance of new vehicles, a combined

injury function for several body regions is applied. However, the 2011 NCAP combines serious

injury risks (AIS3+) for the head, chest, and neck body regions with moderate injury risk (AIS2+)

for the knee-thigh-hip (KTH) region. In this research, AIS3+ injury risk for all the body regions was combined instead to ensure consistency relative to the threat to life of the predicted injury risks.

The body regions in the above definition of CIR are mainly representative of the regions seriously injured in frontal crashes. In an EFP implementation for other crash modes, CIR would include the probability of serious injury for additional body regions such as the abdomen in side crashes, exclude body regions such as the femur for rear crashes, or even add upper extremities in frontal crashes, to address such injuries in the field when corresponding injury risk functions become available (i.e., when developed by the biomechanics research community).

3.2. EFP Processes

An overview of the EFP methodology is presented in Figure 3-1. The processes for the methodology are outlined below.

Figure 3-1. EFP Overview

3.2.1. Real World and Crash Test Databases Analysis Process

1. Identify crash mode of interest, i.e., Frontal, Side, Rear, or Rollover.

2. Establish crash configurations for identified crash mode and corresponding exposure

based on structural engagement from real-world distributions in the National Automotive

Sampling System Crashworthiness Data System (NASS CDS).

3. Select and set up the fleet partner vehicles to represent existing vehicle fleet segments.

4. Utilize data from crash tests representative of the crash configurations of interest to

validate and verify the vehicle and occupant models.

3.2.2. Modeling and Analysis Process

1. Perform vehicle structural modeling:

o Simulate single- and two-vehicle crashes of target and fleet vehicles in representative crash configurations.

o Predict crash pulse, dynamic crush, and intrusions in target and fleet partner vehicles. 2. Estimate occupant injury risk:

o Conduct occupant simulations, utilizing models of Anthropomorphic Test Devices (ATDs), more commonly known as dummies, to predict probabilities of serious

injuries (AIS 3+) in target and partner vehicles in each simulated crash incident.

o Conduct occupant simulations and predict serious injury probabilities over modeled crash configurations and impact speeds.

3.2.3. Safety Prediction Process

The injury risks for occupants of the target vehicle and of the fleet partner vehicles are combined to compute the total injury risk for a given target vehicle. With a baseline or modified/new vehicle design as the target, the total safety of occupants in the target vehicle and partner vehicles (termed fleet societal injury risk in this research) is estimated in single- and two- vehicle crash configurations as follows:

1. Use frequencies of real-world crash occurrence to establish the weighting factor for each

crash incident:

o A crash incident is a given configuration at a given impact speed for a given occupant size in a given seating position.

o The weighting factors are derived from NASS CDS. 2. Sum the risk of serious injuries in both the target and partner vehicles for each crash

incident to calculate the societal injury risk of the crash incident.

3. Calculate total fleet safety, i.e., fleet societal injury risk, by computing an accumulated

injury risk, i.e., the weighted sum of the injury risks for all vehicles across all simulated

crash incidents.

Changes in overall societal, target, or partner injury risk between baseline and modified vehicle designs could be established and evaluated to guide future safety research efforts. A schematic presentation and corresponding equation for societal risk computation in the frontal crash mode is shown in Figure 3-2 and Equation 3-3.

Figure 3-2. Schematic Presentation for Societal Risk Computation in Frontal Crashes

( ) = 푇 ( ) 퐽�퐽𝐽� 퐾𝐾𝐾𝐾 퐿𝐿𝐿�퐿� 푀�푀푀푀𝑀푀 푁푁𝑁푁�푁 푃 푃푃�푃푃푃 푆푆�𝑓𝑓𝑓� 푣 ∑�=1 ∑�=1 ∑�=1 ∑�=0 ∑�=1 ∑�=1 ∑�=1 푤𝑗𝑗𝑗� 푣 ∗ ( ) (3-3)

퐶𝐶𝑗𝑗𝑗� 푣 3.3. Hypothesis/Assumptions

The underlying hypothesis is that the process is grounded in the physical world with sufficient detail, i.e., derived from the real world of crashes with sufficient granularity in the various components of the methodology.

3.3.1. Integral Feature of EFP

An integral feature of EFP is the Real World “Sanity” Checks, i.e., continuous checks with real-world data (NASS CDS and crash test data) to verify and guide the procedure. The crash configurations to be simulated must be based on real-world crash distributions and exposure based on NASS CDS analyses. Also, crash test data that are representative of the crash configurations of interest should be available to validate and verify the vehicle and occupant models, which are the cornerstones of the methodology. The main strength and predictive

potential of EFP is routed in that the approach is based on physical and realistic models, configurations and exposure, injury risks, etc.

3.3.2. Finite Element Models

A critical component for this process is that FE structural models of the target vehicle and partner vehicles representing the fleet are realistic and verified in representative crash configurations up to impact speeds under investigation.

3.3.3. Occupant Response Models

Another critical component for this process is that occupant models with current restraint systems for both the target vehicle and the partner vehicles are realistic and verified in representative crash configurations up to the impact speeds under investigation.

3.4. Crash Data Sources and Overview of Crash Environment

To establish the methodology and conduct the initial implementation, it is important to focus on a single crash mode. In the United States (U.S.), there are two national traffic crash databases, the Fatality Analysis Reporting System (FARS) and the National Automotive Sampling System

(NASS). FARS started in 1975 and is a census of all fatal crashes occurring on U.S. roads. FARS data is from Police Accident Reports (PARs) and contain basic information about the crash, vehicles, and persons involved. NASS, the second U.S. national traffic crash database, started in

1988 and is a stratified sample of police-reported crashes of all severities. NASS is composed of two systems: the General Estimates System (GES) and the Crashworthiness Data System (CDS).

NASS GES data are a nationally representative probability sample selected from all police- reported crashes (around 55,000 cases per year). Each GES case contains a weighting factor that is used to extrapolate the individual cases to the national numbers. GES data incorporates pre-

event, occupant, vehicle, environment, and police assessment of injury information. A principal benefit of GES is to determine the frequency of crashes of various categories, regardless of their severity. The vast majority of cases in GES involve only property damage. The data for GES comes largely from police reports of the crash, which do not include the nature of injuries or their causes. NASS CDS is a nationally representative stratified sample of tow-away crashes of light vehicles with oversampling of recent model vehicles (less than 5 years old) and more severe

injury crashes that occur on U.S. roads, around 4500-5000 cases per year. In addition to police-

reported information, CDS includes detailed vehicle, crash scene, and occupant data, such as

scene diagrams and photographs, vehicle damage, occupant injury, and injuring contacts, which

allow study of injury mechanisms. Each CDS case contains a weighting factor that is used to

extrapolate the individual cases to the national numbers. In order to obtain nationally

representative averages, only NASS CDS weighted data can be used. However, as the data is

excessively disaggregated, the weighting factors can cause distortions. NHTSA provides an

annual compilation of U.S. motor vehicle crash data from FARS and NASS GES (NHTSA 2014).

This compilation is analyzed to establish the vehicle classes to be modeled for a representative

crash-involved EFP vehicle fleet. The analysis also establishes the predominant crash mode and

crash events to be modeled for the initial implementation of the EFP methodology.

Table 3-1 presents the distribution of the national estimate of crashes from GES and the

distribution of fatal crashes from FARS in 2012 by vehicle class or body type. The light vehicle

fleet (i.e., passenger cars and light trucks with curb weight ≤ 10,000 lbs.) represents about 90% of

all crashes on U.S. roads and makes up over 79% of the fatal crashes. As such, the field data

supports modeling a fleet of light vehicles in an initial implementation of the EFP methodology.

It is worth noting that while large trucks are involved in only 3.4% of the crashes, they contribute

8.5% of the fatal crashes. 26

Table 3-1. Vehicles Overall and Fatal Crash Involvement by Body Type (2012 FARS & NASS GES)

Percent Percent Vehice Body Type All Crashes Fatal Crashes Fatal Fatal Passenger Cars 5,576,000 56.4% 18,092 40.7% Light Trucks 3,810,000 38.5% 17,254 38.8% Large Truck > 10,000 lbs. 333,000 3.4% 3,802 8.5% Motorcycle 112,000 1.1% 5,080 11.4% Buses 55,000 0.6% 251 0.6% Total 9,886,000 100% 44,479 100% Note: Other/Unknown Body Type correspond to 2.5% of Fatal Crashes

To investigate the distribution of crash modes on U.S. roads, 2012 GES and FARS were investigated by initial point of impact (IPI) as coded in the two national crash test databases

(Figure 3-3).

Figure 3-3. Areas of IPI in GES and FARS data

Figure 3-4 and Figure 3-5 show the distributions of crash-involved and fatal passenger cars and light trucks (≤ 10,000lbs) by initial point of impact for single and multi-vehicle crashes.

Crash-involved vehicles include vehicles from GES. Fatal vehicles, i.e., crash-involved vehicles with fatalities, are vehicles from FARS. The predominant crash mode for both vehicle classes for both crash-involved and fatal vehicle crashes is frontal impact.

Passenger Car: All Crashes Passenger Car: Fatal Crashes 2012 GES- Initial Point of Impact 2012 FARS- Initial Point of Impact 70% 70%

60% 60% Multiple Vehicle=2,926,000 Multiple Vehicle=10,690 50% Single Vehicle=974,000 50% Single Vehicle=7,402

40% 40%

30% 30% % Total Crashes Total % Crashes Total % 20% 20%

10% 10%

0% 0%

Figure 3-4. Passenger Cars Crash Involvment by IPI: All Severity and Fatal Crashes

Frontal crashes account for 60% of fatal passenger car vehicle crashes followed by fatal side crashes at 24% on U.S. roads. Even though rear impacts are 25% of all crashes for passenger cars, they make up only 7.2% of fatal crashes as shown in Figure 3-4. Similarly, frontal crashes account for 64.1% of fatal light truck crashes followed by side crashes at 16.5%. Rear impacts are

27.4% of all crashes for passenger cars, but make up only 6.4% of fatal crashes as shown in

Figure 3-5.

Light Trucks: All Crashes Light Trucks: Fatal Crashes 2012 GES- Initial Point of Impact 2012 FARS- Initial Point of Impact 70% 70%

60% 60% Multiple Vehicle=3,169,000 Multiple Vehicle=9,663 50% Single Vehicle=642,000 50% Single Vehicle=7,591

40% 40%

30% 30% % Total Crashes Total % Crashes Total % 20% 20%

10% 10%

0% 0%

Figure 3-5. Light Trucks Crash Involvement by IPI: All Severity and Fatal Crashes

The non-collision impacts, which are mainly rollovers, are elevated for light trucks and account for 9.1% of fatal crashes. Some of the crashes with frontal and side IPI involve a rollover subsequent to the planar event. This results in a total of 15.5% of fatal passenger crashes and 28

28.4% of fatal light truck (utility and pickup vehicles) crashes in which a rollover occurred at

initial impact or subsequent to a planar impact.

Frontal crashes account for the highest numbers of fatalities and are thus the primary crash

mode for the initial implementation of the EFP methodology. Frontal crashes are also most

readily addressed with the available vehicle structure and occupant simulation models and injury

metrics as noted in Section 3.5 below. Once EFP feasibility is established by application to

frontal crashes, the methodology is fundamentally extensible to apply to other crash modes, i.e.,

side and rear impacts.

When considering crash events, single-vehicle crashes account for 11.9% of passenger cars

and 10.3% of light trucks involved in frontal crashes as compared with multiple-vehicle crashes,

which account for 40.6% and 39.7% correspondingly. However, single-vehicle crash events

account for 26.4% of fatal passenger car crashes and 26.0% of fatal light truck crashes with

frontal IPI. As such, both single- and multiple-vehicle crashes should be addressed in the

implementation of EFP to frontal impacts.

Figure 3-6 shows the distribution of crash-involved and fatal vehicles by partner pairs in two- vehicle crashes (which are the majority of multiple- vehicle crashes).

Two Vehicle Crashes* by Vehicle Class 2012 GES & FARS 70%

All Crashes= 3,412,423 Fatal Crashes=7,423 60%

50%

PC: Passenger Car 40% LT: Light Truck HT: Heavy Truck > 10,000 lbs. 30% *Excluding % of Total motorcyles, buses, 20% & unknowns

10%

0% PC-PC PC-LT PC-HT LT-LT LT-HT HT-HT

Figure 3-6. Two-Vehicle Crashes: All Severity and Fatal Crashes

The light vehicle target-partner pairings account for 93.3% of two-vehicle crashes in the field and account for 73.5% of fatal two-vehicle crashes. Light vehicle target-partner pairings are thus the primary events to be simulated in the initial implementation of EFP. It is worth noting that while two-vehicle crashes involving heavy trucks make up only 6.7% of the crash-involved two- vehicle crashes, they account for 26.5% of fatal two-vehicle crashes.

3.5. Fleet Vehicle Models for EFP Implementation to Frontal Crash Mode

As noted in section 3.3, the main hypothesis of the EFP methodology is that its predictive potential is rooted in the approach being based on physical and realistic vehicle and occupant models, and representative crash configurations and exposure. As such, it is critical that the finite element structural models of the target vehicle and partner vehicles representing the fleet and the occupant models, including the restraint systems, are realistic and verified in representative crash configurations up to impact speeds under investigation. Also, a consistent approach for modeling and simulating the various crash configurations across the range of speed and occupant size should be adopted.

3.5.1. FE Models Source

Finite element models for crash simulations for more than a dozen passenger cars, sport

utility vehicles, pickup trucks, and single-unit and tractor-trailer combination trucks have been

developed by reverse-engineering at the National Crash Analysis Center (NCAC) since the mid-

1990s for NHTSA and the Federal Highway Administration (FHWA). These models vary in

complexity and size depending on their applications and have been successfully used by

researchers worldwide. The models have been mainly developed for and validated in the full

engagement frontal configuration using data from 56 km/h (35 mph) vehicle-to-barrier crash tests collected through the New Car Assessment Program (NCAP) by NHTSA. As such, these models were suitable candidates to be used for the implementation to frontal crashes. A selected subset of passenger cars and light truck vehicle models is chosen to represent segments of the existing fleet in this research. The selected models and their status at the initiation of this research are presented in Table 3-2.

Table 3-2. Selected FE Models for Representing Partner Vehicles (Initial status)

FEM Weight Initial FEM Status Vehicle Model No. Parts/Elements

2010 Toyota Yaris • Model under development Yaris (MY 2005 – Expected frontal NCAP • current) validation 1100 kg 917/ 1,514,068

2001 Ford Taurus • Different versions validated to frontal NCAP, side NCAP, IIHS Taurus ODB, and roof crush tests (MY 2000 – 2007) • Model includes vehicle interior components 1505 kg 802/ 973,351

2003 Ford Explorer • Validated to frontal NCAP Explorer • Interior components available (MY 2002 – 2005) but not included 2025 kg 923/ 714,205

2007 Chevy Silverado Silverado • Validated to frontal NCAP test (MY 2007 – • Interior digitized but not yet current)

incorporated in the model 2270 kg 719/ 963,48

The FE models of the 2nd generation Toyota Yaris (model year 2005–current), 4th generation

Ford Taurus (model year 2000–2007), 3rd generation Ford Explorer (model year 2002–2005), and

GMT900 platform Chevrolet Silverado (model year 2007–current) are designated as surrogate

vehicles for crash partners in this research. They are used to represent a small passenger car,

midsize passenger car, midsize sport utility vehicle, and a full size pickup truck, respectively, in

the existing vehicle fleet. NHTSA reports the median age of a passenger vehicle on U.S. roads to

be 13.8 years and of a light truck to be 14.5 years (Lu 2006). Thus, these four popular vehicles

provide a reasonable representation of their vehicle segments in the current on-road fleet. It is

important to note that these FE models were developed in planar, i.e., non-rollover, and non-

oblique frontal impacts. Consequently, the initial application of EFP is focused on planar non- oblique frontal crashes involving light vehicles.

3.5.2. FE Model Readiness and Decoupling of Occupant Modeling

The reverse-engineered NCAC FE vehicle models, including the selected models for application in this research, have been developed to match only vehicle structure accelerations

(e.g., compartment crash pulse, engine and brake caliper accelerations), barrier load force, and overall energy balance in a full barrier NCAP frontal test. To date, the vehicle models typically have minimal and varying detail for vehicle interiors (e.g., seat, door panels, and dash) and do not include the occupant or restraint systems. To conduct the occupant simulations in EFP, the relevant vehicle interior and restraint systems need to be modeled and validated. As noted in

Table 3-2, not all the selected FE models had the needed interiors modeled to effectively perform occupant simulation in frontal crash configurations. Given the lack of sufficient FE vehicle interior and restraint models, the approach for the initial implementation of the EFP methodology was to decouple the occupant modeling from the vehicle structural modeling and perform occupant simulations separately. To support the development of the EFP methodology, an effort was initiated at the NCAC to further develop the selected vehicles’ FE interior components to supplement the surfaces and characteristics in the MADYMO occupant model environments, as outlined in Section 3.14 on occupant model development.

3.5.3. Vehicle FE Model Extended Validations

To ensure that the selected vehicle FE structural models could be applied across various frontal impact scenarios to be simulated in EFP, additional validation efforts were initiated and completed at the NCAC for the Yaris, Taurus, Explorer, and Silverado FE vehicle structural models (D. Marzougui, R. Samaha and C. Cui, et al., Extended validation of the finite element 33

model for the 2010 Toyota Yaris Passenger Sedan, NCAC 2012-W-005 2012), (D. Marzougui, R.

Samaha and C. Cui, et al., Extended validation of the finite element model for the 2001 Ford

Taurus Passenger Sedan, NCAC 2012-W-004 2012), (D. Marzougui, R. Samaha and F. Tahan, et al. 2012), (D. Marzougui, R. Samaha, et al. 2012). The scope of the extended validation efforts was based on the availability of crash test data and included: full frontal wall impacts, Insurance

Institute for Highway Safety (IIHS) moderate frontal offset deformable barrier (ODB) impacts, and offset rigid pole impacts. Rigid pole tests were available only for the Taurus. Overall, simulation results compared well to data from these tests and supported the validity of the enhanced models. Vehicle kinematics, accelerometer output data, exterior vehicle crush data, and intrusion responses from the simulations compared well to those of the crash tests.

3.6. NASS CDS Analyses: Methods and Research Design

Descriptive statistical analyses of NASS CDS data are conducted to assess and establish data selection criteria and sample sizes for the field frontal crash population in this research. The crash population is used to identify and define the frontal crash configurations and weighting factors for the EFP simulation matrix. NASS CDS data are also analyzed to identify the body regions for occupant injury risk assessment and to estimate cumulative serious injury and fatality risks in the real-world frontal crashes represented by the EFP simulation matrix.

3.6.1. Field Crash Data Population

To characterize frontal crashes in the U.S., NASS CDS 1995-2011 calendar years involving vehicles model years 1985 or later equipped with frontal airbags and sustaining frontal damage or side damage forward of the A-pillar are analyzed. The objective of the population selection is to obtain a balance between including the most modern vehicle subset while having a reasonable data population size (raw data) for the subpopulation analyses. For crash exposure and developing 34

the real-world weighting factors for EFP, the NASS CDS population is selected to provide an

adequate data sample size of vehicles with modern restraints for weighted data analysis and to

allow binning in the different frontal crash modes, vehicle segments, and occupant age groups

under study. For injury rates, the analyses focus on the population of modern vehicles, 2000-

2011 model years. Model year 2000 is selected to represent the threshold for vehicles designed to

pre-2011 U.S. regulatory and consumer information requirements. The majority of MY 2000-

2011 vehicles tested had reasonable ratings in the consumer information and regulatory testing:

over 85% of MY 2000-2011 vehicles had four- or five-star ratings in the pre-2011 U.S. NCAP frontal testing and around 74% of MY 2000-2011 vehicles had acceptable or good ratings in the

IIHS moderate overlap frontal testing.

As noted previously, NASS CDS is a nationally representative sample of tow-away crashes of light vehicles on U.S. roads with around 4500-5000 cases per year. The NASS CDS national estimates are calculated by applying a weighting factor for each case called the Ratio Inflation

Factor (RIF), which is the product of inverse probabilities of selection in a three-stage sampling process (Radja 2012). CDS is suitable for this research as it includes detailed information on occupant injury of all severities, vehicle damage, and estimates of crash severity metrics, such as change in velocity (delta-V) and Barrier Equivalent Speed (BES), which are computed by crash investigators using crash reconstruction codes. As noted earlier, AIS ranges between 1 (minor injury) to 6 (maximum severity injury) and codes the severity of each individual injury by body region based on the threat to life posed by the particular injury (AAAM 1990). The Maximum

Abbreviated Injury scale injury for an occupant is defined as MAIS.

In this research, maximum recorded moderate and serious injuries, or fatalities MAIS2+F and

MAIS3+F, are considered. Unknown injuries, MAIS7, were excluded from this dataset. The highest recorded injury to a given body region, irrespective of the total number of injuries 35

sustained to that body region, is referred to as the Body Region AIS (BAIS). The following body

regions are considered: head (including face), neck (including cervical spine), thorax, abdomen,

spine (excluding cervical spine), upper extremities, lower extremities (knee-thigh-hip excluding

foot and ankle), and foot and ankle.

Vehicles are divided into five categories representing the light vehicle fleet, i.e., curb weight

≤ 4,536 kg. The mass categories are based on the median mass of 2000-2007 model year vehicles

in fatal crashes: light and heavy passenger cars, light and heavy light trucks and vans (truck-based sport utility vehicles and pickups) (Kahane 2012).

BES is the speed at which a crashed vehicle would have to strike a rigid barrier to absorb the same amount of crush energy as in the actual crash involved (Sharma, et al. 2007). In this research, BES is utilized as a measure of crash severity rather than the traditional delta-V because it is available for a larger number of NASS CDS cases and also provides a more comparable severity measure across various degrees of frontal engagement (i.e., full engagement, offset, small overlap, etc.).

For a comprehensive characterization of frontal impact in the field, crashes with direction of force 11 to 1 o’clock, both vehicles with frontal damage and side damage forward of A-pillar, i.e., close to the front end, were considered in order to capture small overlap, angular frontal collisions. In crashes with an offset mode, only the left side offset was considered such that the driver is seated on the same side of the front-end damage, i.e., struck side. Crashes involving 3+ vehicles are small in number, not well defined, and too varied for simulation.

Two driver age groups, 16-50 and >50 years old, are considered to provide populations with similar, i.e., more consistent, injury tolerance. It has been shown that adult occupant injury tolerance decreases with age and the elderly group has a higher risk of injury than the younger age group at any given crash delta-V. (Zhou, Rouhana and Melvin 1996) (Augenstein, Digges, et 36

al., Investigation of the performance of safety systems for protection of the elderly 2005) (Digges,

Dalmotas and Prasad 2013).

The following defines the frontal crash data population used for this research:

CRASH

• Direction of Force (DOF) 11, 12, or 1 o’clock

• Single- or two-vehicles crashes only (multiple-vehicle crashes excluded)

VEHICLE

• Light vehicles MY 1985 and later equipped with frontal airbags (curb weight ≤ 4,536 kg)

• Vehicle class (or segment)

o PC (Passenger Car) < 1,405 kg

o PC ≥ 1,405 kg

o LT (Light Truck: Sport Utility Vehicle or Pickup) < 2084 kg

o LT ≥ 2084kg

o Vans

• Vehicles with general area of damage (GAD) to the front and side of the vehicle forward

of the A-pillar

• Cases with the following CDS variables available: Direct damage width (DIRDAMW),

location of direct damage relative to the centerline of the vehicle (DVD), average track

width (ATW), and, for vehicles with side damage, wheelbase (WHEELBAS)

• Cases with Barrier Equivalent Speed (BES) available

• Vehicles involved in a rollover were excluded

• Vehicles with secondary impact with extent of damage greater than 2 were excluded 37

OCCUPANT

• Belted driver 16 ≤ age ≤ 50 and age > 50

• Vehicles with drivers fully ejected from the vehicles were excluded

3.6.2. NASS CDS Case Weights

Initial analysis of the data subpopulations highlighted distortions in the injury rates and cumulative injury distributions that could bias results and conclusions. An example of an extreme distortion is shown in Figure 3-7, which presents the cumulative distribution of BAIS2+ injuries to drivers greater than 50 years old. In this subpopulation, several cases were identified with elevated national case weights with one case weight over 34,000 and accounting for almost two- thirds of all the moderate foot injuries. It is not realistic for one or two cases to account for more than half of all injuries. Nevertheless, when NASS CDS data is excessively disaggregated, the national weighting factors can cause such distortions.

Figure 3-7. National Estimates of BAIS2+ injuries to older drivers based on originally assigned NASS weights

Other researchers have adopted different approaches to address extremely large and influential NASS case weights and thus reduce potential bias due to the inclusion of these cases in the final population for analysis. In a study of advanced technology frontal airbags, Bahouth et al. 38

simply removed NASS cases with weighting factors above 3,275 from their study population of

NASS CDS calendar years 1997-2005 (Bahouth, et al. 2007). The threshold of 3,275 was two standard deviations away from the mean case weight of 452 and included 98% of their data population. In another study to develop a multivariate logistic regression model to predict the probability that a crash-involved vehicle will contain occupants with serious or incapacitating injuries using NASS CDS calendar year 1999–2008 data, Kononen et al. also removed NASS cases with weights larger 5,000 (where their population mean weight was 314) (Kononen,

Flannagan and Wang 2011). They cited previous experience using NASS CDS data, indicating that cases with weights greater than 5,000 are usually extreme outliers that often exert a large influence on resulting model parameter estimates and standard errors. In the current research, in order to maintain the study sample size, a new approach was adopted by reassigning overly influential weights to the 95th percentile value rather than excluding corresponding cases from the

analysis. Given that NASS has a stratified sampling scheme, it would be challenging to evaluate

the representativeness of the national estimates computed using the case weights assigned in

NASS. There is no census of crash injury data in the United States to compare against predicted

national estimates. However, the method developed in this research is a consistent approach that

is in accordance with the CDS sampling scheme and can be applied to any population of study in

NASS CDS. This method is described in the following section.

3.6.3. Summary of New Approach to Address Overly Influential NASS Weights

The new approach to identify and address overly influential CDS weights first involves a

hardcopy review of NASS cases in the study population with large weights, as determined by the

statistics of the mean weight by vehicle class and injury level. The objective of the review is to

identify and remove miscoded cases. This is followed by trimming, i.e., reassignment, of large

case weights to a maximum of the 99th percentile CDS case weight (initially) by MAIS level and

vehicle class, which is a consistent approach and in accordance with the NASS sampling scheme

at the case selection level (Samaha, Prasad and Nix, Opportunities for Injury Reduction in US

Frontal Crashes: An Overview by Structural Engagement, Vehicle Class, and Occupant Age

2013). For the finalized population for this research, a reassignment of large case weights to a

maximum of the 95th percentile CDS case weight is adopted.

3.6.4. NASS CDS Case Weights Trimming: Data Analysis and Results

NASS case weights, i.e., the weighting factors that designate the national estimates for each

sampled case, are initially identified as candidates for outliers if their contribution to the mean

weight at a given BES by MAIS level is much elevated relative to the mean weights in its

vicinity, i.e., ±1 km/h from the BES of interest. A mean weight of 350 at a given BES can be

interpreted as, on average, each driver involved in a crash at that BES within NASS CDS

represents the crash experience of 350 other tow-away crash involved drivers at the given BES.

The distributions of NASS mean weights by BES for the MAIS3+F, MAIS2, MAIS1, and MAIS0

case populations are used to identify cases with overly influential weights (Figure 3-8 through

Figure 3-11). An approach to reassigning the NASS case weights based on vicinity mean by BES is examined.

Figure 3-8. NASS mean weights – distribution by BES for MAIS3+F case population

Figure 3-9. NASS mean weights – distribution by BES for MAIS2 case population

Figure 3-10. NASS mean weights – distribution by BES for MAIS1 case population (Note: mean weight at BES=6 is 65,279)

As a demonstration, in the plot for MAIS3+F mean weights (Figure 3-8), the spikes at certain

BES values are indicative of possible overly influential case weights. Eleven cases for MAIS3+F,

shown in Table 3-3, were identified as candidate cases for weight reassignment to the BES vicinity mean weight. Note that there are two such cases each at a BES of 14 and 26 km/h.

Figure 3-11. NASS mean weights – distribution by BES for MAIS0 case population

Table 3-3. Candidate NASS CDS MAIS 3+F cases for weight reassignments

NASS Case No. BES (km/h) BAIS3+ Weight 1999. 76. 58 14 876 head 2004. 5.171 14 1273 lowerxtr 2006. 5. 62 22 896 upperxtr 1999. 41.156 26 1134 lowerxtr 2000. 75. 63 26 2802 thorax 2001. 12.166 28 909 upperxtr 2002. 8. 86 42 1288 head 2009. 43.261 51 1929 thorax 2007. 11. 29 55 1280 upperxtr 2007. 48.252 94 173 head 2006. 48.260 101 288 lowerxtr

Reassigning weights based on vicinity mean by BES, although intuitive, entails a subjective

interpretation of the weights and is time-consuming. An alternative and more objective approach

of trimming overly influential weights is needed and was investigated for this research. NASS

CDS has a three-stage sampling scheme. The first stage is geographical, i.e., selection of PSUs

(Program Sampling Units). The second stage involves selection of police jurisdiction and the third stage involves selection of individual cases within a police jurisdiction. At any given police jurisdiction, “each crash is classified into a stratum based on type of vehicle; most severe police reported injury, disposition of the injured, tow status of the vehicles and model year of the vehicles” [Radja 2012]. The first four stratum categories in the NASS CDS third stage sampling are actually (1) by type of vehicle, i.e., passenger cars, light trucks and vans, and (2), (3), and (4) categories are related to injury level and disposition of the injured, e.g., transported to a medical facility and/or hospitalized. As such, a NASS weight reassignment scheme based on statistics of mean weights by vehicle type and injury severity was deemed to be more applicable and in accordance with the NASS CD sampling scheme. It is assumed that the weights should have a normal distribution.

Descriptive statistics by vehicle class for MAIS3+F, MAIS2, MAIS1, and MAIS0 were

computed for the study population (Table 3-4 through Table 3-7). For the majority of the entries, the maximum values exceed the 99th percentile values, which are typically three standard deviations from the mean (for normal data distributions).

Table 3-4. Descriptive statistics of NASS weights for MAIS3+F case population

Mean Mean Vehicle Weight Class Std 95th 99th N Mean Lower Upper Max MAIS3+F Dev %tile %tile 95% CL 95% CL Passenger Car ≥1405 kg 250 83 57 109 209 301 703 2802 Passenger Car <1405 kg 333 90 71 110 184 359 922 1929 Light Truck ≥2084 kg 133 85 61 109 140 316 724 997 Light Truck <2084 kg 89 80 53 107 128 253 724 724 Van 58 89 50 127 145 398 794 794

Table 3-5. Descriptive statistics of NASS weights for MAIS2 case population

Mean Mean Vehicle Weight Class Std 95th 99th N Mean Lower Upper Max MAIS 2 Dev %tile %tile 95% CL 95% CL Passenger Car ≥1405 kg 288 155 122 189 290 582 1923 2407 Passenger Car <1405 kg 425 285 117 453 1763 690 2886 34791 Light Truck ≥2084 kg 45 171 64 277 353 506 2133 2133 Light Truck <2084 kg 171 180 130 229 330 756 1791 2439 Van 66 197 67 327 530 938 3704 3704

Table 3-6. Descriptive statistics of NASS weights for MAIS1 case population

Mean Mean Vehicle Weight Class Std 95th 99th N Mean Lower Upper Max MAIS 1 Dev %tile %tile 95% CL 95% CL

Passenger Car ≥1405 kg 1422 326 288 365 738 1198 3002 16978 Passenger Car <1405 kg 1918 452 381 523 1582 1644 4259 57872 Light Truck ≥2084 kg 305 305 153 458 1351 1010 2875 22106 Light Truck <2084 kg 783 394 204 583 2705 1057 2507 65279 Van 326 332 260 404 662 1387 3196 7282

Table 3-7. Descriptive statistics of NASS weights for MAIS0 case population

Mean Mean Vehicle Weight Class Std 95th 99th N Mean Lower Upper Max MAIS 0 Dev %tile %tile 95% CL 95% CL Passenger Car ≥1405 kg 873 754 583 925 2579 2234 9107 57872 Passenger Car <1405 kg 1020 891 776 1005 1867 3178 7933 25029 Light Truck ≥2084 kg 201 384 295 472 638 1232 2886 5366 Light Truck <2084 kg 540 629 529 730 1187 2155 6237 11404 Van 193 640 410 869 1617 2000 13110 16340

Next, a hardcopy review of the cases with very large weights, and thus, candidate outliers,

was performed to identify any possible miscoding. Following are the case weight thresholds for

the hardcopy reviews, based on the NASS weights descriptive statistics, specifically the 99th

percentile and maximum values by MAIS level:

• > 1,000 for MAIS 3+ (6 cases)

• > 2,000 for MAIS 2 (12 cases)

• > 20,000 for MAIS 0 and 1 (7 cases)

Four cases, shown in Table 3-8, were identified as miscoded and were removed from the

dataset. For the remaining cases, the NASS weight, i.e., Ratio Inflation Factor (RIF), was limited

to the 99th percentile of the mean case weights by MAIS level and vehicle class.

Table 3-8. Cases identified as miscoded and removed from analysis

BES NASS Case No. BAIS Notes (km/h) Weight

miscoded as frontal: case No 1998.48.139 24 57872 review & photos show Injury undercarrige damage

miscoded belt use: spider AIS 2 pattern damage indicate 2002.45.21 44 5400 upper upper limb flailing injuries xtr consistent with unbelted

miscoded belt use: low AIS 3+ speed pole crash, 2000. 75. 63 26 2802 thorax consistent with unbelted or very late firing of air bag AIS3+ 2009. 43.261 51 1929 severe override thorax

The effects of reassigning the original NASS weights to the 99th percentile of the mean case

weights by MAIS level and vehicle class are shown in Figure 3-12 through Figure 3-15. The

BAIS3+ injuries to drivers aged between 16 and 50 years old based on the originally assigned and trimmed NASS weights are shown in Figure 3-12. Both of the two influential cases, 2000.75.63

and 2009.43.261, that were identified as miscoded and removed from the study involved AIS 3+

thorax injuries for the younger drivers. Removal of these two cases led to a substantial drop in the

cumulative number of thorax injuries in the study population.

Figure 3-12. BAIS3+ injuries to younger drivers based on originally assigned and trimmed NASS weights to 99th percentile of mean

The BAIS3+ injuries to drivers greater than 50 years old with the originally assigned and

trimmed NASS weights are shown in Figure 3-13. Both show similar cumulative injury data levels but there is a slight reduction in the lower extremities and thoracic injuries in the study population.

Figure 3-13. BAIS3+ injuries to older drivers, based on originally assigned and trimmed NASS weights (99th percentile of mean)

The BAIS2+ injuries to drivers aged between 16 and 50 years old based on the originally assigned and trimmed NASS weights are shown in Figure 3-14. Several cases were identified that exceeded the limit of 2000, one of which was miscoded. The removal of the miscoded case and the trimming of weights caused a substantial decrease in upper extremity injuries and smaller decreases in thoracic injuries.

Figure 3-14. BAIS2+ injuries to younger drivers based on originally assigned & trimmed NASS weights (99th percentile of mean)

The BAIS2+ injuries to drivers greater than 50 years old for the originally assigned and

trimmed NASS weights are shown in Figure 3-15. Several cases were identified that exceeded the established limit of 2000, one of which had a weight over 34,000 and accounted for almost two- thirds of all the moderate foot injuries.

Figure 3-15. BAIS2+ injuries to older drivers based on originally assigned and trimmed NASS weights (99th percentile of mean)

A comparison of mean NASS weights for the MAIS3+F population for originally assigned and trimmed NASS weights is shown in Figure 3-16. This data demonstrates that further trimming case weighting factors to the 95th percentile mean case weights will better address highly influential case which could lead to inconsistent and distorted injury distributions and rates for CDS subpopulations of interest.

Figure 3-16. Mean NASS weights for MAIS3+F population: Originally assigned versus trimmed study NASS weights

For example, with the 99th percentile trimmed case weights, the contribution of full

engagement crashes to moderate injuries (AIS3+F) for older drivers is inconsistently high relative 48

to that of the frontal offset in vehicle model years 2000-2100 as compared with model years

1985-1999, as shown in Figure 3-17. Further trimming of the NASS case weights to the 95th

percentile mean case weights, by MAIS level and vehicle class, results in more consistent injury

distributions, as shown in Figure 3-18.

Figure 3-17. Crash distributions by crash mode and model year for older drivers (*effect of overly influential NASS weights)

Figure 3-18. Crash distributions by crash mode (*effect of overly influential NASS weights, 99th percentile and 95th percentile of mean)

In future research, the data in Figure 3-16 suggest performing hardcopy review of additional highly influential cases based on their contribution to vicinity mean weights, to identify if there any more miscoded cases. However, for this research, the data in Figure 3-19 through Figure 3-22

indicate that trimming NASS case weights to the value of 95th percentile of the mean weight per

MAIS level and vehicle class yields consistent cumulative injury distributions for the subpopulations of interest.

Figure 3-19. BAIS3+ injuries to younger drivers based on trimmed NASS weights (both 99th percentile and 95th percentile of mean)

Figure 3-20. BAIS3+ injuries to older drivers, based on trimmed NASS weights (both 99th percentile and 95th percentile of mean)

Figure 3-21. BAIS2+ injuries to younger drivers, based on trimmed NASS weights (both 99th percentile and 95th percentile of mean)

Figure 3-22. BAIS2+ injuries to older drivers, based on trimmed NASS weights (both 99th percentile and 95th percentile of mean)

3.6.5. Frontal Impact Taxonomy: Basis for EFP crash configurations

To determine and prioritize the frontal crash configurations to be simulated for the target and partner vehicles in this research, distributions of real-world frontal crashes were established from the NASS CDS based on a new classification of the post-crash damage profile to the front end.

The novel classification includes frontal small offset impacts with side damage, as coded by

NASS CDS, and thus allows a more comprehensive and realistic classification of frontal crashes

(Samaha, Prasad and Nix, Opportunities for Injury Reduction in US Frontal Crashes: An

Overview by Structural Engagement, Vehicle Class, and Occupant Age 2013).

A Frontal Impact Taxonomy (FIT), which is based on structural engagement, is applied to allow assessment of the relative crash involvement and injury distributions of real-world crash modes. Each NASS case is classified based on frontal structural damage profile, estimated location of the front rails, and side damage forward of the A-pillar. Sullivan originally developed the taxonomy of frontal crash damage through an analysis of NASS CDS 1995-2005 data

(Sullivan, Henry and Laituri 2008). The taxonomy included eight frontal crash classifications and was proven to be robust as it yielded consistent distributions for multiple subsets of the 1995-

2005 data. Significantly, Sullivan developed a methodology for determining the likely rail engagement, which is not recorded in the NASS data. Scullion expanded the Sullivan taxonomy to classify between rail impacts and degree of offset impacts (P. Scullion, et al. 2011). In this research, an expanded FIT was developed to include small overlap impacts with side damage, as coded by NASS CDS. The expanded taxonomy is implemented to ensure that all front crashes are captured and to overcome the CDS coding challenges relative to frontal crashes with narrow overlap that has been highlighted by earlier research (Pintar, Yoganandan and Maiman 2008)

(Sherwood, Nolan and Zuby 2009). The new FIT includes four distinct classification groups, shown in Figure 3-23 and detailed below, and an “Other” group. It allows the assessment of the contribution of corner impacts and the other defined groups (“Full Engagement”, “Offset”,

“Between Rail”, and “Other”) to the overall frontal crash population.

Figure 3-23. Study Frontal Impact Taxonomy (FIT) Groups

Full Engagement: Both vehicle frame rails engaged, i.e., direct damage spans the estimated location of the two rails.

Offset: One vehicle frame rail engaged, i.e., direct damage overlaps location of one rail, left or right side.

Corner: Defined as the combination of two groups, small overlap front and small overlap side.

• Small overlap front: vehicle with frontal damage and direct damage located entirely

outside of the vehicle frame rails, left or right side

• Small overlap side: vehicle with side damage where direct damage is forward of the A-

Pillar

Other: Vehicles with underride damage or frontal damage and 9, 10, 2 or 3 o’clock direction of force or not otherwise classifiable.

Small Overlap Side Population Development: NASS CDS cases with side damage and

DOF 11, 12, or 1 o’clock are considered. An approach is established to estimate the location of the center of direct damage relative to the front end of each crashed car. An overview of the

approach is demonstrated in Figure 3-24 with the corresponding SAS2 (Statistical Analysis

System) code provided in Appendix A. Since the length of a vehicle is not available in CDS, the front end for a crashed vehicle was estimated based on wheel base length and front track width obtained from the vehicle specification data compiled by Transport Canada (Transport Canada

2012). A review of track width and wheel base length data for over 9000 MY 1985 to MY 2011 vehicle models, with wheel base length within the range of 240-290 cm, indicated that front track widths are equal to 50-60% of wheel base length.

Figure 3-24. Region for side damage for FIT

Cases with center of direct damage forward of the center of the front wheel were determined to be a good approximation of cases with direct damage forward of the A-pillar and were classified in the “Small Overlap Side” frontal group. Although all the cases with side damage forward of the center of the front wheel were grouped as one population for this study, an overview analysis indicated that the direct damage for these cases were in general evenly distributed from the wheel toward the estimated front end for both the younger and older driver for both model year ranges. Table 3-9 shows the frontal impact taxonomy groups for the younger population in MY 2000-2011 vehicles.

2 Software developed by SAS Institute for data management, statistical and mathematical analysis and other applications. 54

Table 3-9. FIT with Small Overlap Side Groups: SideFront00-SideFront36 for Younger Driver and MY2000- 2011 vehicles, (99th percentile Trimmed NASS Weights)

Population Weighted Estimates MY 2000-2011 16 ≤ AGE ≤ 50 All MAIS All MAIS % MAIS % All Crashes 3+F Crashes 3+F 3+F Between Rail 382 53 110,727 3,479 9.2% 18.2% FullEng 1,592 91 544,076 7,348 45.4% 38.5% Offset 1,134 87 408,015 6,598 34.1% 34.6% Other(Front) 195 7 69,195 152 5.8% 0.8% SideFront00 10 - 1,821 0 0.2% 0.0% SideFront04 7 2 1,349 58 0.1% 0.3% SideFront08 22 5 6,826 598 0.6% 3.1% SideFront16 13 2 2,341 187 0.2% 1.0% SideFront20 17 1 9,227 24 0.8% 0.1% SideFront24 26 - 9,659 0 0.8% 0.0% SideFront28 20 1 7,141 166 0.6% 0.9% SideFront32 9 - 4,098 0 0.3% 0.0% SideFront36 3 1 433 254 0.0% 1.3% Sml Offset 72 7 22,637 224 1.9% 1.2% Total 3,502 257 1,197,546 19,089 100.0% 100.0%

3.7. Field Data Analyses: Crash Configurations and Body Regions

In this section, the distributions across crash configurations, by FIT and vehicle class for the

younger and older driver populations in MY 1985+ and MY 2000-2011 vehicles involved in U.S.

frontal crashes, are presented. All data shown in the tables and graphs, unless noted otherwise,

are weighted NASS national estimates using the trimmed study weights (to 95th percentile of the mean).

3.7.1. Overall Sample and Weighted Data Populations

The new frontal impact taxonomy is applied in NASS CDS for the selected vehicle classes and driver age groups to establish the data populations to be analyzed for crash involvement and injury rates and distributions in this research. The raw sample, i.e., unweighted, data are presented in Table 3-10 for the younger driver and in Table 3-11 for the older driver. The corresponding weighted data, i.e., national estimates, using the trimmed weights, are presented in Table 3-12 and

Table 3-13.

Table 3-10. Younger Driver Sample Population: MY 1985+ Airbag Equipped Vehicles, 1995-2011 NASS CDS

Passenger Car Passenger Car Light Truck Light Truck Van 16 ≤ Age ≤ 50 ≥3106lb <3106lb ≥4594lb <4594lb All MAIS3+F NASS Sample MAIS MAIS MAIS MAIS MAIS Crashes 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F Between Rail 183 34 262 47 28 5 81 9 18 2 669 97 FullEng 863 43 1321 95 235 17 575 39 223 16 3427 210 Offset 548 41 915 83 128 12 382 29 124 16 2278 181 Other(Front) 90 4 147 3 30 0 68 0 24 4 370 11 Sml Offset Fro 33 2 38 8 16 1 32 3 18 2 153 16 Sml Offset Sid 74 8 107 13 5 1 23 4 20 0 255 26 Total 1791 132 2790 249 442 36 1161 84 427 40 7152 541

Table 3-11. Older Driver Sample Population: MY 1985+ Airbag Equipped Vehicles, 1995-2011 NASS CDS

Passenger Car Passenger Car Light Truck Light Truck Van Age > 50 ≥3106lb <3106lb ≥4594lb <4594lb All MAIS3+F NASS Sample MAIS MAIS MAIS MAIS MAIS Crashes 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F Between Rail 90 23 54 9 8 0 21 8 8 3 224 43 FullEng 354 45 241 35 49 10 154 18 81 10 997 118 Offset 253 38 191 37 35 7 116 19 54 7 757 108 Other(Front) 46 6 45 2 4 0 19 1 5 1 129 10 Sml Offset Fro 15 0 7 0 5 1 10 2 3 0 43 3 Sml Offset Sid 32 9 31 6 7 0 13 2 6 1 107 18 Total 790 121 569 89 108 18 333 50 157 22 2257 300

Table 3-12. Younger Driver Weighted Population: MY 1985+ Airbag Equipped Vehicles, 1995-2011 NASS CDS

Passenger Car Passenger Car Light Truck Light Truck Van 16 ≤ Age ≤ 50 ≥3106lb <3106lb ≥4594lb <4594lb Weighted Data All MAIS3+F (National MAIS MAIS MAIS MAIS MAIS Crashes Estimates) 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F

Between Rail 54349 1656 105576 3143 10315 130 24102 383 5558 54 205266 5366 FullEng 267827 3291 561753 6651 45911 853 161111 3053 74409 1105 1125963 14953 Offset 202588 2395 451668 5584 32711 1766 135595 1621 39114 1511 874553 12878 Other(Front) 33083 126 60680 170 8787 0 19527 0 4656 156 127184 451 Sml Offset Fro 14263 113 16556 853 3796 39 19812 62 4686 73 60252 1141 Sml Offset Sid 28695 615 41139 1444 1871 36 9622 317 5314 0 89052 2412 Total 600805 8196 1237371 17844 103390 2824 369768 5436 133736 2899 2482270 37200 MAIS3+F rate 1.3% 1.4% 2.7% 1.4% 2.1% 1.5%

Table 3-13. Older Driver Weighted Population: MY 1985+ Airbag Equipped Vehicles, 1995-2011 NASS CDS

Age > 50 Passenger Car Passenger Car Light Truck Light Truck Van Weighted Data ≥3106lb <3106lb ≥4594lb <4594lb All MAIS3+F (National MAIS MAIS MAIS MAIS MAIS Crashes Estimates) 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F 0-2 3+F Between Rail 31154 1473 15952 544 1613 0 7317 427 2577 222 61277 2665 FullEng 101950 2811 89676 2813 8524 795 46512 1155 23424 956 278618 8531 Offset 81454 2100 74041 2473 9646 263 35107 1623 19031 251 225990 6711 Other(Front) 16269 746 10330 35 408 0 6991 140 3751 149 38820 1070 Sml Offset Fro 6383 0 971 0 630 105 2368 263 1694 0 12414 368 Sml Offset Sid 12245 456 11886 244 3483 0 3626 59 3884 77 35959 836 Total 249455 7586 202855 6109 24304 1164 101922 3667 54362 1655 653079 20181 MAIS3+F rate 3.0% 2.9% 4.6% 3.5% 3.0% 3.0%

Descriptive statistics for driver injury distributions in the crash configurations classified by

the new Frontal Impact Taxonomy are highlighted in Table 3-14 and Table 3-15 for the younger and older driver populations.

Table 3-14. FIT Configurations: Serious Injury Distribution and Rates for Younger Driver, MY 85+ airbag equipped vehicles

Weighted Data Rate of All Crashes MAIS 3+F % All % MAIS 3+F 16 ≤ Age ≤ 50 MAIS 3+F Between Rail 205,266 5,366 8.3% 14.4% 2.6% FullEng 1,125,963 14,953 45.4% 40.2% 1.3% Offset 874,553 12,878 35.2% 34.6% 1.5% Corner 149,304 3,552 6.0% 9.5% 2.4% Other(Front) 127,184 451 5.1% 1.2% 0.4% Total 2,482,270 37,200 100% 100% 1.50%

Table 3-15. FIT Configurations: Serious Injury Distribution and Rates for Older Driver, MY 85+ airbag equipped vehicles

Weighted Data Rate of All Crashes MAIS 3+F % All % MAIS 3+F Age > 50 MAIS 3+F Between Rail 61,277 2,665 9.4% 13.2% 4.3% FullEng 278,618 8,531 42.7% 42.3% 3.1% Offset 225,990 6,711 34.6% 33.3% 3.0% Corner 48,374 1,204 7.4% 6.0% 2.5% Other(Front) 38,820 1,070 5.9% 5.3% 2.8% Total 653,079 20,181 100% 100% 3.1%

3.7.2. Frontal Impact Crash Configurations

The predominant groups in the frontal crash population under study are the full engagement,

offset, and between rails configurations accounting for 45.4%, 35.2%, and 8.3% of for the 57

younger driver in model year 1985 and later vehicles, and 42.7%, 34.6 %, and 9.4% for the older

driver (shown in Table 3-14 and Table 3-15). Full engagement frontal crashes result in 40.2% of

all serious injuries to belted front seat occupants; offset frontal crashes result in 34.6%; and

between rails crashes result in 14.4%. Similar serious injury distributions are seen for the older

drivers. Corner impacts, i.e., small overlap front and side, account for only 6.0% of all crashes for

the younger population, but they result in 9.5% of the serious injuries.

The trends in the distributions for crash exposure and injury crashes by crash configurations in modern vehicles, i.e., MY 2000-2011 model years, are similar to the trend in the overall population of vehicles, i.e., MY 1985 and later (shown in Table 3-16 and Table 3-17). There seems to be an increase in the relative contribution to the serious injury crashes for the between

Rail crash configuration in modern vehicles.

Table 3-16. FIT Configurations: Serious Injury Distribution and Rates for Younger Driver, MY 2000+ airbag equipped vehicles

Weighted Data Rate of 16 ≤ Age ≤ 50 All Crashes MAIS 3+F % All % MAIS 3+F MAIS 3+F MY 2000+ Between Rail 103,807 3,006 9.5% 18.2% 2.9% FullEng 489,102 6,104 45.0% 36.9% 1.2% Offset 371,155 5,782 34.1% 34.9% 1.6% Corner 60,472 1,512 5.6% 9.1% 2.5% Other(Front) 63,259 152 5.8% 0.9% 0.2% Total 1,087,794 16,556 100% 100% 1.52%

Table 3-17. FIT Configurations: Serious Injury Distribution and Rates for Older Driver, MY 2000+ airbag equipped vehicles

Weighted Data Rate of Age > 50 All Crashes MAIS 3+F % All % MAIS 3+F MAIS 3+F MY 2000+ Between Rail 33,701 1,656 9.9% 16.4% 4.9% FullEng 133,183 4,203 39.3% 41.7% 3.2% Offset 120,040 3,428 35.4% 34.0% 2.9% Corner 26,656 650 7.9% 6.4% 2.4% Other(Front) 25,156 140 7.4% 1.4% 0.6% Total 338,735 10,077 100% 100% 3.0% 58

In summary, crash involvement for the various crash configurations is similar in modern

vehicles and for both younger and older drivers. Consequently, the predominant frontal crash

groups (full engagement, offset, and between rails configurations), which constitute close to 90%

of all frontal crashes, are used for the simulations in the implementation of EFP for frontal

crashes in this research. Moreover, corner impacts present complex crash scenarios for simulation

and require further development and validation of the existing FE models.

3.7.3. Crash Involvement by Direction of Force (DOF)

In NASS CDS, as part of the Collision Deformation Classification (CDC), the principle

direction of force (DOF) denotes the resultant forces acting on a vehicle in a crash as designated

by the hour sectors of a conventional clock positioned on the vehicle (Standard, S. A. E 1980).

The DOF designates the principle angle of the crash for a vehicle. For example, a 10 o’clock

DOF crash denotes a principle resultant force on the vehicle in a range 45-75 degrees, i.e., an

oblique left side direction (shown in Figure 3-25).

Figure 3-25. NASS CDS Direction of Force (DOF)

The predominant DOF in the frontal crash population under study is 12 o’clock (i.e., collinear if the crash involves two vehicles) for the three main configuration of interest as shown in Figure

3-26 and Figure 3-27, for both crash exposure and serious injury crashes.

Figure 3-26. Frontal Crash Involvement by DOF

Figure 3-27. Serious Injury Frontal Crashes by DOF

3.7.4. Serious Injuries by Body Region and Driver Age Group

The distributions of serious injury by body region (BAIS3+) in the frontal crash study

population are presented in Figure 3-28 for the younger and older age groups in modern vehicles

(MY 2000-2011). The numbers are the weighted NASS population counts, i.e., actual national estimates for body region injuries.

Figure 3-28. Serious Injuries by Body Region for Modern Vehicles (* designate small sample size)

The lower extremities, followed by the upper extremities, thorax, and head are the most prevalently injured body regions for younger drivers, 16 ≤ age ≤ 50. The thorax, followed by the lower extremities, upper extremities, head, and neck are the most prevalently injured body regions for older drivers, age > 50. The rate of serious injuries by body regions for younger versus older drivers in modern vehicles are shown in Table 3-18. These are the rates of injuries for each body region per driver (or per crash) in the study population. The denominators of the rates are the total number of drivers by driver age for the modern vehicle grouping. The entries marked with ‘-’ are not computed due to small data sample size (~ less than 18 ) and as such corresponding rates based on NASS weighted data, i.e., national estimates, for these populations are questionable (Ellen Hertz 2001). The sample population breakdown for cases with serious injuries by body region is provided in Appendix B, Table B1.

Table 3-18. Serious Injury Rates by Body Region

Upper Lower Driver Head Neck Thorax Abdomen Extr Extr 16 ≤ Age ≤ 50 0.20% - 0.23% 0.10% 0.41% 0.87% Age > 50 0.54% 0.28% 1.42% - 0.50% 1.10%

The older population has higher rates of serious injury than the younger population for all the

body regions under consideration. The increased injury rate for older drivers is most prominent

for the thorax, for which the rate of serious thoracic injury to older drivers in MY 2000-2011

vehicles is over 6 times greater than that to younger drivers, and the head, for which the rate of

serious head injury to older drivers is almost 2 times greater than that to younger drivers.

Moreover, older drivers have an overall serious injury of 3.0% for modern vehicles involved in

frontal crashes, as compared with 1.52% for younger drivers, as shown in Table 3-14 and Table

3-15 in section 3.7.1 above.

3.8. Frontal Crash Population for EFP Implementation

The older driver demonstrates increased fragility, i.e., higher injury rates, across body regions in the frontal crash population, as demonstrated in Section 3.7.4. Thus, different injury risk functions are required for older drivers than for younger drivers. Crash injury risk functions by body region for the older population are still under research by the biomechanics community, while such functions have been developed and applied for the younger population and the corresponding ATD, i.e., dummy, in regulatory and consumer information crash testing protocols

(as discussed in section 3.13 below). To provide a population with more consistent injury tolerance, the younger driver age group of 16-50 years is chosen to be simulated in this initial implementation and proof-of-concept of EFP.

In the EFP formulation, societal risk computation is segregated by single- and two-vehicle crash events. For completeness, the distribution of both single- and two-vehicle crashes is examined for the two age groups and is shown in Table 3-19. While two-vehicle crashes are predominant at over 80% for both driver age groups, the younger driver has a relatively higher involvement in single-vehicle frontal crashes.

Table 3-19. Frontal Crash Population by Driver Age and Crash Event

MY 1985+, Airbag Single vehicle Two Vehicle Equipped Vehicles Crashes Crashes

16 ≤ age ≤ 50 17.1% 82.9%

age > 50 13.7% 86.3%

The FE vehicle models available to model the four EFP fleet partner vehicle segments have

been developed and validated for impact speeds of current consumer information testing, 35 mph

(56 km/h) for U.S. NCAP frontal full barrier test and 40 mph (64 km/h) for IIHS ODB test

protocols. Simulations utilizing these models at higher speeds would be an extrapolation beyond

their range of performance. While the frontal crash populations presented so far include crashes

across all crash severities, the data in Table 3-20 shows that crashes with BES up to 40 mph

represent 99.7% of all frontal crashes for younger drivers and account for 88.9% of serious

injuries in this crash mode. Consequently, the simulations in EFP will be performed up to 40

mph, thus representing the vast majority of frontal crashes on U.S. roads while within the

performance range of available FE vehicle models.

Table 3-20. Frontal Crashes MY 1985+ Airbag Equipped Vehicle

16 ≤ Age ≤ 50 All Crashes MAIS3+F Crashes All BES 2,482,270 37,200 BES ≤ 40 mph 2,475,770 33,086 % up to 40 mph 99.7% 88.9%

To establish the EFP weighting factors (i.e., real-world frequency of occurrence) for the crash configurations, crash exposure distributions are developed for the FIT configurations for the younger driver in single- and two-vehicle crash events. The EFP fleet segments, based on

availability of FE vehicle models, include light and heavy passenger cars and light and heavy

light trucks, but not vans. The crash exposure distribution for EFP by FIT and crash event for MY

1985 and later vehicles is presented in Table 3-21. The full engagement and offset crash

configurations are predominant for both single- and two-vehicle crashes; however the between rails configuration account for a much larger percentage of single-vehicle crashes as compared with two-vehicle crashes.

Table 3-21. Younger Driver, FIT by Crash Events, MY 1985+ Vehicles, BES ≤ 40 mph, EFP Fleet Segments

MY 1985+, air bag Single Two Single Two % of Single % of Two equipped, younger All Crashes Vehicle of Vehice of Vehicle Vehicle Vehicle Vehicle driver, BES ≤40mph Total Total Between Rail 198,038 100,144 97,893 25% 5% 4.3% 4.2% FullEng 1,047,944 128,322 919,621 32% 47% 5.5% 39.3% Offset 832,745 123,813 708,932 31% 37% 5.3% 30.3% Other(Front) 122,372 22,431 99,941 6% 5% 1.0% 4.3% Sml Offset F&S 139,232 28,949 110,283 7% 6% 1.2% 4.7%

Total 2,340,330 403,659 1,936,671 100% 100% 17.2% 82.8%

The distribution by FIT and crash event for the modern vehicles shown in Table 3-22 is

similar overall to the model year 1985+ vehicle population albeit with a small tradeoff increase

for full engagement crashes as compared with frontal offsets. However, the weighted population

for modern vehicles is determined to provide sparse sample sizes in the development of EFP

weighting factors for impact speed bins for single-vehicle crash simulations (as discussed in

section 3.12 “NASS CDS Impact Speed EFP Weighting Factors” below). The weighted crash

population for MY 1985+ single-vehicle crashes is over two times the size of the single-vehicle

crash population for the modern vehicles (403,659 vs. 195,925), as shown in Table 3-21 and

Table 3-22.

Table 3-22. Younger Driver, FIT by Crash Events, MY 2000+ Vehicles, BES ≤ 40 mph, EFP Fleet Segments

MY 2000+, air bag Single Two Single Two % of Single % of Two equipped, younger All Crashes Vehicle of Vehice of Vehicle Vehicle Vehicle Vehicle driver, BES ≤40mph Total Total Between Rail 100,492 48,548 51,944 25% 6% 4.7% 5.1% FullEng 458,578 70,626 387,951 36% 47% 6.9% 37.8% Offset 349,456 54,377 295,080 28% 36% 5.3% 28.8% Other(Front) 61,244 10,530 50,715 5% 6% 1.0% 4.9% Sml Offset F&S 56,145 11,844 44,301 6% 5% 1.2% 4.3%

Total 1,025,916 195,925 829,991 100% 100% 19.1% 80.9%

For frontal impacts, the current ATDs (or dummies) that are available and validated both as

physical devices for crash testing and as virtual models for computer simulation are the Hybrid III

(HIII) dummies representing the 50th percentile (%tile) male occupant and the 5th %tile female

occupant (Humanetics 2014) (TASS 2009) (TASS 2009). While the 50th %tile male dummy is

used as the driver occupant in both the U.S. NCAP frontal full barrier test and IIHS ODB test

protocols, the simulations in the EFP will be performed for both the 50th %tile male and 5th %tile

female drivers to assess the safety performance of a target vehicle for a more realistic and wider

segment of the driving population. To establish the EFP weighting factors for the two occupant

sizes modeled, crash exposure distributions are developed by driver height groupings. The

standing heights for the 50th %tile male and 5th %tile female dummies are 175cm (5’ 9”) and 150

cm (4’ 11”) with a mid-point of 163cm. Young drivers of height up to 163 cm in modern airbag

equipped vehicle models accounted for 25.6% of occupants involved in frontal crashes, as

presented in Table 3-23. The cumulative distribution plot is shown in Figure 3-29. Consequently,

in EFP, it is assumed that the 50th %tile male dummy represents 75% of the population and the 5th

%tile female dummy represents 25% of the population. This relative distribution of crash

involved occupant by size is similar to findings by prior research (Laituri, Kachnowski, et al.

2003).

Table 3-23. Cumulative Frontal Crashes by EFP Occupant Height Grouping

Cumulative Cumulative NASS Population (BES ≤ 40mph) Height Exposure MAIS3+F 16 ≤AGE≤ 50, belted only, MY 85+ 163 cm 23.8% 24.2%

AGE >50, belted only, MY 85+ 163 cm 29.5% 31.9% 16 ≤AGE≤ 50, air bag equipped & 163 cm 25.6% 27.9% belted, MY 2000+ Age > 50, air bag equipped & 163 cm 31.2% 42.2% belted MY 2000+

Figure 3-29. Cumulative Frontal Crash Involvement by Driver Height

3.9. Research Design of EFP Simulation Matrices

For the initial implementation of EFP, the simulation matrices are designed to address the predominant real-world frontal impact configurations as identified previously. The schematic for the crash configurations to be simulated is provided in section 3.2, Figure 3-2. The simulations include two major crash events and represent two occupant sizes in frontal crashes up to speeds of current regulatory and consumer information testing. The simulation matrices for a given target

vehicle are presented in Table 3-24 and Table 3-25. As the FE vehicle models available to model

the four EFP fleet partner vehicle segments were developed in non-oblique frontal impacts, this

research is focused on collinear single- and two-vehicle crashes. For the initial application of

EFP, collinear crashes provide a good representation for the field data since the predominant

DOF in the frontal crash population under study is 12 o’clock for both crash exposure and serious

injury crashes (shown in section 3.7.3).

Table 3-24. Single-Vehicle Crash Simulations

Note: 15 LS-DYNA Simulations and 30 MADYMO (50%tile male & 5%tile female drivers) Simulations per Target Vehicle

For single-vehicle crashes, three configurations are selected and simulated at five impact speeds (shown in Table 3-24). The NCAP test protocol entails a collinear full engagement impact to a rigid barrier and is representative of a full engagement crash identified by FIT. The IIHS test protocol, while originally designed to simulate a two-vehicle 50% offset impact configuration, entails a collinear impact with a deformable barrier at 40% offset and is a good representation of the FIT offset configuration whereby direct damage overlaps the estimated location of one rail.

The pole frontal center impact is a good representation for a protocol resulting in localized damage that does not engage the vehicle frame rails and is identified as “between rails” by FIT.

For two-vehicle crashes, two collinear configurations are selected and simulated at five impact

speeds, a full engagement impact and a 50% offset head-on impact (shown in Table 3-25). The selected two-vehicle configurations represent the two most predominant crash groups identified by FIT developed in this research.

Table 3-25. Two-Vehicle Frontal Crash Simulations

Note: 40 LS-DYNA Simulations and 160 MADYMO (50%tile male & 5%tile female drivers) Simulations for Target & Partner

Target Vehicle: In this research, passenger cars are considered as the primary target vehicles of interest in the proof-of-concept and initial application of EFP to frontal crashes. A crossover utility vehicle (CUV) is also considered as a target in a case study application of EFP for safety analyses of a lightweight versus baseline fleet composed of two vehicle segments. In general, the

FE model of any new vehicle design being introduced into the fleet (e.g., with lightweight advanced materials, next generation gasoline vehicle, electric vehicle, plug-in hybrid electric vehicle, etc.) could be used as a target vehicle in the EFP formulation. An FE model of an existing vehicle could also be used as a target vehicle.

In the simulation matrices, the occupant responses are used to predict probabilities of serious injuries in the target and partner vehicles for restrained occupants over the modeled crash configurations and impact speeds. The occupant responses serve as the basis for estimating fleet societal risk in EFP.

3.10. NASS CDS Crash Configuration EFP Weighting Factors

An expanded crash population to include vehicles without airbags is examined to establish

the EFP weighting factors. The objective was to further increase the sample size of crashes

occurring in the 5 mph impact speed bins for simulation (as shown in the EFP matrices above).

The cumulative distributions of the weighted subpopulations, i.e., national estimates, for the

binned sample crashes are used to establish the EFP impact speed weighting factors for a given

target vehicle class, as described in the next section. The crash exposure distributions for the

expanded population by FIT and the EFP fleet segments, for the younger driver, are presented in

Table 3-26.

Table 3-26. FIT by Crash Events, MY 1985+ Vehicles, BES ≤ 40 mph, EFP Fleet Segments (No Airbag filter)

Single Two MY 1985+ younger Single Two % of Single % of Two All Crashes Vehicle of Vehice of driver, BES ≤40mph Vehicle Vehicle Vehicle Vehicle Total Total Between Rail 238,409 127,298 111,111 26% 5% 4.4% 3.9% FullEng 1,278,132 152,337 1,125,795 31% 47% 5.3% 39.2% Offset 1,013,166 155,540 857,626 31% 36% 5.4% 29.8% Other(Front) 157,541 25,972 131,568 5% 6% 0.9% 4.6% Sml Offset F&S 187,051 37,464 149,587 8% 6% 1.3% 5.2% Total 2,874,299 498,611 2,375,688 100% 100% 17.3% 82.7%

The crash distributions for single- and two-vehicle crashes in the expanded population are very similar to distributions for the crash population in airbag equipped vehicles (presented in

Table 3-21); however, there is a 24% increase in the population size of single-vehicle crashes from 403,659 to 498,611. An important insight from the analyses of crash exposure for this research is as follows: frontal crash involvement for belted occupants by crash configuration for the two age groups and two occupant sizes is similar for modern vehicles when compared with overall model years and is similar for vehicles equipped with airbags when compared with vehicles not equipped with airbags. These observations also apply to crash involvement over

impact speed bins by vehicle target class. The implication is that the real-world weighting factors

for the EFP model implementation for frontal crashes will remain applicable for the foreseeable

future. However, as the penetration of effective crash avoidance technologies in the fleet increase,

the NASS CDS analyses for crash exposure should be revisited to ensure that crash involvement

by crash configuration is consistent with the distributions of current research.

“Between Rail” frontal crashes account for 26% of all single-vehicle crashes and 5% of two-

vehicle crashes, as shown in Table 3-26. Given the nature of the localized structural engagement,

it is assumed that the two-vehicle real-world crashes categorized in the “Between Rail”

configuration would be better represented with a centerline pole crash simulation. As such, the

“Between Rail” of two-vehicle crashes is folded into the “Between Rail” weighting segment of

the single-vehicle crashes in EFP frontal crash implementation. The EFP weighting factors for the

simulation of the single-vehicle and two-vehicle crash configurations, as outlined in the EFP

matrices above, are highlighted in Table 3-27 below and account for 88% of the frontal crash

population under study.

Table 3-27. FIT by Crash Events, EFP Fleet Segments (No Airbag filter), with Between Rail folded into Single-Vehicle Crashes

MY 1985+ Single Two Single Two % of Single % of Two younger driver, All Crashes Vehicle of Vehice of Vehicle Vehicle Vehicle Vehicle BES ≤40mph Total Total Between Rail 238,409 238,409 - 39% 0% 8.3% 0.0% FullEng 1,278,132 152,337 1,125,795 25% 50% 5.3% 39.2% Offset 1,013,166 155,540 857,626 26% 38% 5.4% 29.8% Other(Front) 157,541 25,972 131,568 4% 6% 0.9% 4.6% Sml Offset F&S 187,051 37,464 149,587 6% 7% 1.3% 5.2% Total 2,874,299 609,723 2,264,576 100% 100% 21.2% 78.8%

3.11. NASS GES EFP Weighting Factors for Vehicle Class Exposure

For two-vehicle crash simulations, the weighting factors for crash configuration are modulated by crash exposure to each vehicle class. The crash exposure by vehicle class for the

crash partners for both passenger car <3106 lbs. and ≥3000 lbs., shown in Table 3-28, is based on the 2010 NASS GES crash exposure from the NHTSA 2010 Traffic Safety Facts (NHTSA 2012).

Crash exposure in two vehicle crashes by vehicle class is similar in the most current GES data available, shown in Table 3-29, and is not expected to change substantially in the near future. For the passenger car (PC) class, a 50/50 distribution of PCs <3106 lbs. and PCs ≥3106 lbs. and a

50/50 distribution for SUVs and pickups in the light truck (LT) class are assumed, shown in

Table 3-30. In addition, no bias for frontal crash pairings as compared to other vehicle to vehicle crash modes was assumed, given the availability of crash pairing data in Traffic Safety Facts.

These assumptions can be reevaluated in future implementation of the EFP.

Table 3-28. 2010 NASS/GES Vehicle Class Crash Exposure

Two Vehicle Crash Distributions Based on 2010 Traffic Safety Facts (DOT 811659, Table 30) PC-PC PC-LT PC-HT LT-LT LT-HT HT-HT Total 32.7% 45.2% 3.4% 16.3% 2.2% 0.2% 100% Note: HT are heavy trucks (> 10,00lbs)

Table 3-29. 2012 NASS/GES Vehicle Class Crash Exposure

Two Vehicle Crash Distributions Based on 2012 Traffic Safety Facts ( DOT HS 812032, Table 30) PC-PC PC-LT PC-HT LT-LT LT-HT HT-HT Total 33.1% 44.1% 3.8% 16.1% 2.6% 0.4% 100%

Table 3-30. Crash Partner Pairings

Crash Partner Pairings in Study Two Vehicle Crash Simulations

LC/LTSUV 11.3% LC/HC 16.3% LC/LTPU 11.3% LC/LC 8.2% HC/LTSUV 11.3% HC/HC 8.2% HC/LTPU 11.3% Total 32.7% Total 45.2% Note: LC=Light PC, HC=Heavy PC, LTSUV= Light SUV, and LTPU=Light PU 71

3.12. NASS CDS Impact Speed EFP Weighting Factors

In the EFP formulation, the impact speed weighting factors are established by binning the

cumulative distributions of BES for a target vehicle for the simulated frontal crash configurations.

Since passenger cars are considered as the primary target vehicles of interest in this research,

cumulative BES distributions for the two categories of passenger cars [PC < 3,106 lbs. (1,405 kg)

and PC ≥ 3,106 lbs. (1,405 kg)] are developed by crash configuration and crash event. Ideally, cumulative distributions of actual impact speed are needed to weight the simulations; however

impact speeds are not available in NASS CDS. Establishing the actual impact speed for a given crash requires a reconstruction of the crash which is not performed by the NASS accident investigators. In this initial implementation of EFP, bins of distributions of BES are used as surrogates for bins by impact speed. To examine the appropriateness of this approach, BES was

× computed as , where is energy absorbed by the vehicle structure in the 2 퐸푎푎푎푎푎푎푎푎 �𝑣ℎ푖�푖� 𝑚𝑚 퐸�푎𝑎�푎𝑎 impact, from the FE simulation outputs for the simulations described in the EFP matrices in

section 3.9 (for each vehicle target studied), and compared with the impact speed simulated. As noted in section 3.6.1, the barrier equivalent speed computation is based on crush energy

(Sharma, et al. 2007).

The computed BES for the single- and two-vehicle EFP simulations for an example heavy PC target, the Ford Taurus, are shown in Table 3-31 and Table 3-32. By definition, BES is the speed at which a crashed vehicle would have to strike a rigid barrier to absorb the same amount of crush energy as in the actual crash simulation. As expected, the computed BES most closely matches the impact speed for the NCAP, i.e. full rigid barrier impact simulations. Although there are vehicle front end stiffness and geometric compatibility considerations, the computed Taurus BES values in impacts with heavier vehicles are higher than the impact speeds of the simulations, the

computed Taurus BES values in impacts with lighter vehicles are lower than the actual impact

speeds, as expected in general. Overall, given the ranges used for BES in the development of weighting factors later in this section, BES is a reasonably good surrogate for impact speed in the initial implementation of EFP.

Table 3-31. Computed BES for Taurus Target in EFP Single-Vehicle Crash Simulations

Impact Taurus Baseline BES Speed Target (mph) (mph) 15 14 20 19 NCAP 25 24 30 28 35 33 20 14 25 18 IIHS 30 21 35 26 40 30 15 14 20 19 CenterPole 25 23 30 28 35 33

Table 3-32. Computed BES for Taurus Target in EFP Two-Vehicle Crash Simulations

Impact Impact Taurus Baseline BES Taurus Baseline BES Speed Speed Target (mph) Target (mph) (mph) (mph) 15 19 15 14 20 25 20 19 Explorer Full 25 30 Yaris Full 25 23 30 35 30 27 35 40 35 30 15 19 15 11 20 24 20 14 Explorer Offset 25 29 Yaris Offset 25 21 30 36 30 26 35 40 35 29 15 17 15 14 20 23 20 19 Silverado Full 25 30 Taurus Full 25 23 30 34 30 29 35 38 35 33 15 17 15 13 20 24 20 19 Silverado Offset 25 30 Taurus Offset 25 23 30 34 30 27 35 54 35 31

The crash population of MY 1985+ vehicles for younger drivers, as presented in Table 3-27, is analyzed and the corresponding BES distributions are presented in Figure 3-30, Figure 3-31, and Figure 3-32.

Figure 3-30. Cumulative BES Distributions of Full Engagement Frontal Crashes for PC Targets, Young Drivers

Figure 3-31. Cumulative BES Distributions of Offset Frontal Crashes for PC Targets, Young Drivers

Figure 3-32. Cumulative BES Distributions of Full Engagement Frontal Crashes for PC Targets, Young Drivers

The data in the figures shows that the majority of the frontal crash exposure is at the lower speeds and further supports the choice of impact speed range simulated for EFP. Due to the differences in the distributions, the data also supports implementing separate impact speed weighting factors for single-vehicle crashes and two-vehicle crashes. The BES distributions for the older drivers in the study crash populations are also analyzed and compared with the corresponding distributions for the younger drivers in the passenger car categories. The overlays of BES distributions for older and younger drivers are presented in Figure 3-33 and Figure 3-34 for two-vehicle full engagement and offset crashes.

Figure 3-33. Older vs. Younger Drivers Full Engagement Two-Vehicle Frontal Crash BES Distribution

Figure 3-34. Older vs. Younger Drivers Offset Two-Vehicle Frontal Crash BES Distribution

The data shows that both driver age groups have reasonably similar crash exposure by crash configurations and BES distributions, although older drivers are more involved in two-vehicle crashes than younger drivers when compared with single-vehicle crashes (refer to Table 3-19 in section 3.8). Consequently, the population for both age groups was combined for the binning of

BES distributions by crash configuration and crash event, to develop the impact speed weighting factors for passenger car targets. This results in a 26% increase in population sample size and a

20% increase in the weighted population for frontal crashes with passenger cars as the target, thus resulting in smoother and more consistent BES distributions, especially for single-vehicle crashes

(shown in Figure 3-35, Figure 3-36, and Figure 3-37 below). Note that the sample size N is provided for each population in the supporting figures.

Figure 3-35. Cumulative BES Distributions of Full Engagement Frontal Crashes for PC Targets, All Drivers

Figure 3-36. Cumulative BES Distributions of Offset Frontal Crashes for PC Targets, All Drivers

Figure 3-37. Cumulative BES Distributions of Between Rail Frontal Crashes for PC Targets, All Drivers

In summary, the expanded population use to develop the EFP impact speed weighting factors

is as follows:

• MY 85+ vehicles in frontal crashes

• No “airbag equipped” filter; only belted drivers

• NASS national estimate factors, i.e., weights, set to 95th %tile by vehicle class and MAIS

level to address overly influential NASS case factors

• One age group driver: Age ≥ 16

The EFP impact speed weighting the single-vehicle and two vehicle crash configurations selected for simulations involving passenger car target vehicles are presented in

Table 3-33 and

Table 3-34 below. The sample sizes per bin for single vehicle crashes are provided in

Appendix B, Table B2 and Table B3. Future refinements of EFP could adapt impact speed

weighting based on review and analysis of the computed BES from finite element simulations for

a given target in the single- and two-vehicle impacts. This will provide a differentiation of BES-

based weighting factors by crash partner and impact configuration, as motivated by the computed

BES results for an example heavy PC shown in Table 3-31 and Table 3-32.

Table 3-33. EFP Impact Speed Weighting for PC Targets: Two-Vehicle Crashes

Two Vehicle Crashes Two Vehicle Crashes Two Vehicle Crashes Full Engagement - All Ages Offset Frontal - All Ages Between Rail Frontal- All Ages Barrier Passenger Passenger Barrier Passenger Passenger Barrier Passenger Passenger Equivalent Car Car Equivalent Car Car Equivalent Car Car Speed ≥3106lb <3106lb Speed ≥3106lb <3106lb Speed ≥3106lb <3106lb 0-12 50.0% 43.8% 0-12 69.6% 63.7% 0-12 44.7% 42.6% 12-17 31.0% 35.9% 12-17 23.0% 23.3% 12-17 35.6% 29.2% 17-22 13.1% 13.6% 17-22 4.5% 9.4% 17-22 9.8% 13.2% 22-27 4.0% 4.9% 22-27 1.6% 2.5% 22-27 5.1% 9.2% 27-32 1.2% 1.0% 27-32 0.9% 0.6% 27-32 1.1% 3.1% ≥32 0.6% 0.8% ≥32 0.5% 0.4% ≥32 3.7% 2.7% Total 100% 100% Total 100% 100% Total 100% 100%

Table 3-34. EFP Impact Speed Weighting for PC Targets: Single-Vehicle Crashes

Single Vehicle Crashes Single Vehicle Crashes Single Vehicle Crashes Full Engagement - All Ages Offset Frontal - All Ages Between Rail Frontal- All Ages Passenger Passenger Passenger Passenger Passenger Passenger BES (mph) Car Car BES (mph) Car Car BES (mph) Car Car ≥3106lb <3106lb ≥3106lb <3106lb ≥3106lb <3106lb 0-12 51.6% 56.3% 0-12 51.6% 48.1% 0-12 36.0% 22.8% 12-17 31.4% 24.2% 12-17 28.7% 35.5% 12-17 29.5% 30.2% 17-22 9.4% 7.1% 17-22 10.9% 9.3% 17-22 14.7% 29.8% 22-27 4.6% 9.1% 22-27 5.0% 3.0% 22-27 11.5% 10.8% 27-32 2.2% 1.2% 27-32 2.1% 2.2% 27-32 2.6% 3.3% ≥32 1.0% 2.2% ≥32 1.7% 1.9% ≥32 5.8% 3.1% Total 100% 100% Total 100% 100% Total 100% 100%

The expanded population provided a sufficient sample size to develop BES distributions in two vehicle crashes for a CUV target vehicle, used a surrogate for new LT designs in the case study application of EFP for safety analyses of a two segment lightweight vs. baseline fleet. The

EFP impact speed weighting for the CUV in the two vehicle crash configurations are presented in

Table 3-35 below.

Table 3-35. EFP Impact Speed Weighting for CUV Targets: Two-Vehicle Crashes

Table 3-36. Expanded Population Size for Two Vehicle Crashes for PC and LT Targets

Full Engagement Frontal Offset Frontal Passenger Car Passenger Car Passenger Car Passenger Car LT≥4594lb LT <4594lb LT≥4594lb LT <4594lb ≥3106lb <3106lb >3106lb <3106lb MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p MAIS 0-2 MAIS3p Sample 1170 84 1734 154 298 22 1001 80 777 72 1208 126 158 17 598 63 size Total 1254 1888 320 1081 849 1334 175 661 Sample National 350522 5619 707768 10693 53998 1004 268396 5895 271562 4579 557239 8534 32596 1497 203015 4474 Estimates Total 356141 718461 55002 274291 276142 565773 34093 207489 Estimate

3.13. Injury Risks Functions for EFP Implementation to Frontal Crashes

Crash injury risk functions are developed by the automotive biomechanics research community. They are statistically derived estimates of injury probabilities that are associated with different level of stimuli, such as forces, moments, accelerations, deflections, or combinations thereof, for a prescribed occupant population and specific body region (Prasad, Mertz, et al.

2010). Crash injury risk functions transform responses from physical and virtual dummies, when subjected to crash loading, into human occupant injury probabilities. Such functions can be updated as more information on injury risk mechanisms becomes available, whether from cadaver and animal testing or field data, and as the corresponding mappings to dummy responses are established.

In EFP, the objective is to implement a combined injury risk metric that would address the most frequently injured body regions in real-world crashes for a given crash mode. In the NASS

CDS analyses presented in section 3.7.4, it was established that older drivers have increased fragility, i.e., higher injury rates, than younger drivers in frontal crashes for all the main body regions, especially for the thorax. Consequently, the two age groups considered in this research would require different injury risk functions for the various body regions. In order to provide a population with more consistent injury tolerance, the younger driver age group of 16-50 years is chosen to be simulated in this initial implementation and proof-of-concept of EFP. It is important 80

to note that, to date, neither the frontal safety regulation nor the consumer information frontal crash testing programs in the U.S. include testing requirements or ratings for older occupants.

Moreover, injury risk functions by body region for the older population are still under research by the biomechanics community and have not yet been mapped to dummy responses in regulatory and consumer information crash testing protocols.

In this research the computation of societal risk in EFP has been designed to easily incorporate new or modified injury risk functions. As such, EFP can incorporate improved or additional injury risk functions by body region, for example lower extremities, as they become available. EFP can also be easily extended to include the computation of societal risk for older occupants once the corresponding injury risk functions become available. This functionality is demonstrated by a what-if scenario for an updated chest injury function in Chapter 4.

The lower extremities, upper extremities, thorax, and head are the most frequently seriously injured body regions for younger drivers in frontal crashes, as shown in section 3.7.4. As such, a combined injury risk metric that incorporates injury risk functions for these four body region is desired for EFP. However, upper extremity injuries have not been well addressed by the biomechanics community in any crash mode and corresponding injury risk functions are not yet available. While neck injuries are considerably infrequent for younger drivers, such injuries are of interest to the biomechanics community and the neck body region was included in the new metric for the 2011 U.S. NCAP program. In this research, a combined injury risk metric that includes the neck along with the head, thorax, and lower extremity body regions is adopted. As presented in 3.1.2, the equation for the frontal EFP combined injury risk metric is as follows:

= (1 1 ( 3 +) 1 ( 3 +) 1

𝑜�표𝑜𝑜� ℎ𝑒� �ℎ𝑒� 퐶�� ( 3−+�) − (푃푃푃1 푏 퐴𝐴( 3� ∗+�)))− 푃푃푃푏 퐴𝐴 � ∗ � − (3-4)

푃푃푃푏𝑛𝑛 퐴𝐴 � ∗ − 푃푃푃푏퐾�퐾 퐴𝐴 81

In this metric, the individual probabilities of serious injuries to the head, neck, thorax, and

knee-thigh-hip (KTH) complex in frontal crashes will be estimated by using state-of-art biomechanical injury risk functions as discussed below. The injury risk functions are driven by the relevant responses output by the occupant modeling for the head, neck, chest, and femur body regions: HIC15 (Head Injury Criteria in 15 ms time interval), Neck Loads, Chest Deflections, and

Femur Loads.

3.13.1. Combined Injury Risk in this Research versus NCAP

The CIR (Combined Injury Risk) metric selected for this research addresses the same body

regions as the recent NCAP metric for frontal impacts and also assumes independence of injury

by body region, which is an established practice in the automotive biomechanics research.

However, a different injury risk function for the neck is implemented as discussed below. Also, as

a novel implementation, the CIR in this research combines serious injury risks (AIS3+) for all the

body regions, including the KTH complex, which is typically included as a moderate injury risk

(AIS2+)? This ensures consistency relative to the threat to life of the predicted injury risks across

body regions. Another novel implementation in this research is the consideration of injury risk

functions for both the 50th %tile male dummy and the 5th %tile female dummy in the driver position and computing societal risk as an aggregate of risk for two occupant sizes.

The injury risk functions adopted in the 2011 NCAP were selected by NHTSA from available

literature prior to the publication of the agency upgrade notice in 2008 (NHTSA July 2008). Since

then, several studies to evaluate the fidelity of the selected injury risk function have been

performed by the biomechanics research community. In 2009, Laituri assessed the NCAP risk

functions for “point” estimate perspective of NCAP-like events and an “aggregate” estimate

pertaining to 0-35 mph crashes, involving modeling a theoretical vehicle whose NCAP

performance approximated a fleet average (Laituri, Henry S. and Sullivan 2009). In 2010, Prasad

evaluated the NCAP risk functions by comparing the predicted injury risk to the four body

regions in 300 NCAP tests against those to drivers in crashes of similar severity in NASS CDS

2013 (Prasad, Mertz, et al. 2010). In 2013, as part of a study to develop an NCAP rating system

for older front seat occupants, Digges applied both the NCAP risk functions and alternative risk

functions that are more representative of the injury tolerance of older occupants to 302 NCAP

tests (Digges, Dalmotas and Prasad 2013). Digges compared the results to the injury risks of

belted occupants in airbag-equipped vehicles (MY 1998-2006) in NCAP-like NASS frontal crashes. In the three studies, the NASS-like populations were crashes representative of a full engagement frontal crash with a change of velocity in the vicinity of that experienced in the

NCAP 35 mph crash test. While the assessment by Laituri and Prasad did not consider age groupings in NASS, Digges segregated the analyses and comparison for front seat occupant into three age groups. The NCAP individual risk functions were also further examined in the current research. Based on this examination and the studies cited, the following functions for the head, neck, femur, and neck have been adopted in the initial implementation of EFP.

3.13.2. Head Injury Risk Function

In the 2009 assessment by Laituri, the NCAP head risk curve demonstrated acceptable fidelity as it produced results within the confidence limits of both the point and aggregate risk estimates. In the 2010 assessment by Prasad, the injury risk predicted from the NCAP was found to be close to that observed in NASS. In the 2013 comparison by Digges, there was general agreement between the NCAP tests and NASS AIS3+ injury risk for the head/face body region, although the results were indicative of younger occupant injury rates. Consequently, the 2011

NCAP head AIS3+ HIC15 risk function was implemented in this research. The risk function is

identical for both the 5th %tile female and 50th %tile male dummies and is as follows:

(3-5)

3.13.3. Neck Injury Risk Function

In the 2011 NCAP, the upper neck risk is defined as the maximum of the Normalized Neck

Injury Criterion (Nij) or axial force. In the assessment by Laituri, the NCAP Nij demonstrated unacceptable fidelity while the NCAP tension-only closely predicted the point estimate. Similarly in the assessments by Prasad and Digges, the neck tension injury risk function was a better predictor of serious neck injuries than Nij in NASS data. In addition, the NCAP AIS3+ risk function for Nij has the threshold issue of computing a probability of 3.8% serious injury risk at

Nij=0, thus overstating the neck injury risk for low values of Nij. Consequently, only the neck tension AIS3+ risk functions are implemented in this research. The functions for the two dummy sizes are as follows:

For the 5th percentile dummy,

(3-6)

For the 50th percentile dummy,

(3-7)

3.13.4. Chest Injury Risk Function

The 2011 NCAP thorax deflection risk curve was selected by NHTSA for a 35 year old

occupant since it was evaluated from a set of age-dependent curves developed earlier by Laituri

(Laituri, Prasad, et al. 2005). In the 2009 assessment by Laituri, the NCAP chest curve yielded an

injury rate within the confidence of the point estimate but was lower than the mean. Similarly, in

the 2010 assessment by Prasad, the injury risk predicted from the NCAP was slightly below the

range estimated from NASS. This is not unexpected as both studies used one age group in their

NASS analyses, given the pronounced dependence of thoracic injuries on occupant age. In the

2013 comparison by Digges, the NCAP chest injury curve yielded risks that are in reasonable

agreement with chest injury risk to the NASS young population considered (15-43 years old).

Consequently, the 201 NCAP thorax AIS3+ risk functions were implemented in this research.

The functions for the two dummies sizes are as follows:

For the 5th percentile dummy,

(3-8)

For the 50th percentile dummy,

(3-9)

3.13.5. KTH Injury Risk Function

In the three studies by Laituri, Prasad, and Digges, the risk function for moderate femur

injuries (AIS2+) was assessed and found to substantially under-predict the range of injury rates

for the Knee-Thigh-Hip (KTH) complex in NASS, by an order of magnitude in the Laituri study.

As noted by Prasad, more research is required to develop a relevant injury measure for this body

region, possibly requiring a combination of bending moments and axial forces rather than simply

the axial load in the femur. In this research, the femur AIS 3+ risk curve from FMVSS No. 208

Advanced Airbags Rule (Kuppa, et al. 2001) (NHTSA 2006) is used, but the threshold (of 1.7% for the 5th percentile female and 0.7% for the 50th percentile male) is removed in order to have

zero risk at zero femur loads. It is expected that the AIS3+ risk function, which is based strictly

on axial femur loads, would also underestimate the real-world serious injury rates. However, until

a better risk function is developed by the biomechanics research community, the state-of-the-art

curve should be used for this important body region. The uncorrected functions from the FMVSS

No. 208 Advanced Airbags Rule are as follows:

For the 5th %tile dummy,

( 3 +) = (3-10) . . . 1 1 퐹�퐹퐹�퐹�퐹 𝑓𝑓� �4 9795−0 47941 � 4 9795 푃 퐴𝐴 1000 − 1+ � For the 50th %tile dummy, 1+ �

( 3 +) = (3-11) . . . 1 1 퐹�𝑚𝑚𝑚 4 9795 𝑓𝑓� �4 9795−0 326 � 1+ � 푃 퐴𝐴 1+ � 1000 − 3.14. Occupant and Restraint Models for Fleet Modeling

Occupant responses serve as the basis for estimating fleet societal risk in the EFP formulation

since they are used to predict probabilities of serious injuries in the target and partner vehicles

over the modeled crash configurations and impact speeds. As discussed earlier, the FE vehicle

models available to model the four EFP fleet partner vehicle segments lack sufficient FE interior

and restraint components to perform integrated FE vehicle and occupant simulations. Therefore, the approach for the initial implementation of EFP to frontal crashes is to decouple the occupant modeling from the vehicle structural modeling and perform occupant environment simulation separately using MADYMO as illustrated in Figure 3-38.

FEM Simulation

MADYMO compartment crash pulse Simulation

AIS3+ Rhead HIC15 AIS3+ Chest Deflection Rchest Femur Load AIS2+ Rlower − ext

Figure 3-38. Decoupled Frontal Impact Occupant Simulations

It is important to note that while the decoupled approach is effective in the initial EFP implementation to frontal crashes, the better practice would be to use an integrated model of the dummy and detailed vehicle interior. An integrated approach would more accurately model intrusions and occupant interactions with the vehicle interior. For example, in an integrated approach, all the components behind the surface of the Instrument Panel (IP) and the steering column components and their attachment to the cross-car beams would be modeled, thus resulting in intrusions of better fidelity in that vehicle region. This enhanced approach is not necessary for the initial implementation to frontal crashes, but would be required for other crash modes such as side impact. The integrated approach for future implementations of EFP is illustrated in Figure

3-39.

Figure 3-39. Future Implementation of EFP Occupant Simulations

Integral Feature of EFP- For either integrated or decoupled vehicle/occupant modeling approaches, an integral feature of the methodology is that crash test data that are representative of the crash configurations of interest are available to validate and verify the occupant models.

Another critical component of EFP is that the occupant models have modern restraint systems, which are realistic and validated in representative crash configurations up to impact speeds under study. Such occupant models allow EFP to effectively predict the safety performance of new vehicle designs in a modern vehicle fleet.

3.15. Framework for EFP Occupant Model Development

As part of this research, a two-part general framework for developing occupant models for fleet vehicles is established: model development (Part I) and model verification and robustness evaluation (Part II), shown in Figure 3-40.

Figure 3-40. General EFP Framework for Occupant Model Development for Fleet Simulations

In Part I, generic occupant environment models with modern restraints, including seat belts, airbags, knee bolster, etc., that have been designed to meet current safety regulations and consumer information testing are obtained from restraint manufacturers. The occupant environments are modified to reflect the interior geometry and clearances of the desired target or partner vehicle. A hypothesis of EFP is that by using generic systems as foundations, the occupant models will be more representative of a vehicle class rather than a specific vehicle. The restraint characteristics and dummy positioning are subsequently adjusted in an iterative process until the simulation results prove to be a realistic match for the crash test results. The available

crash test data are typically regulatory and consumer information tests and research single-vehicle

crash tests, i.e., vehicle-to-object tests, performed by NHTSA, IIHS, and Transport Canada in support of developing new safety regulations and advanced crash safety studies. These tests are

typically representative of the real-world crash environment.

Part II involves model verification and robustness assessment. Model verification includes

comparison of occupant responses from simulation and test data, and refining restraints to achieve

realistic responses from simulations when compared to the crash test data. Model robustness

assessment involves evaluating trends in occupant responses in the modeled crash configurations 89

over a range of two speeds and refining the restraints, as needed, for realistic trends for the occupant sizes modeled.

3.16. Development of the EFP Partner and Target Occupant Model Environments

A research effort to apply the two-part framework for developing occupant frontal model environments for EFP partner and target vehicles of interest was performed at the NCAC in collaboration with renowned industry expert Dr. Priya Prasad3 (R. R. Samaha, P. Prasad and S.

Kamakakkannan, et al. 2013) (R. R. Samaha, P. Prasad and D. Marzougui, et al. August 2014). In this effort, the available FE interior components for the various vehicles are supplemented with

generic surfaces and characteristics from MADYMO occupant model environments. The occupant model development and simulations were performed by NCAC support staff.

The occupant modeling approach utilizes data from the FE analysis simulations, including the occupant compartment geometry, crash pulse, and toe pan intrusions, as input to MADYMO rigid body occupant environments to simulate occupant responses using multibody dummy models.

For the midsize and small passenger car simulations, the toe pan and footrest intrusions are modeled with MADYMO FE elements and prescribed structural motion (PSM) driven by the output from the FE simulations. For the SUV and pickup simulations, the toe pan and footrest intrusions are modeled using planes, to which the accelerations from selected nodes of the FE vehicle toe pan and footrest are applied. The planar approach is used for the larger vehicle models because intrusion in these cases is minimal and the geometry and exact motion of the toe pan and footrest would not significantly affect the occupant responses. A limitation for the occupant models for the initial frontal implementation of EFP is that steering linkage and

3 Dr. Priya Prasad was previously a Technical Fellow in Automotive Safety at Ford Motor Company and is a world- renowned vehicle safety expert in vehicle structure and occupant modeling, biomechanics, and crash injury and data analysis. 90

attachments to the dash have not been modeled given the lack of data availability. As such, the

steering column geometry and crush characteristics are assumed to not change in spite of toe pan

and dash intrusions. While this is true in the vast majority of simulations, especially at the lower

speeds, the assumption may not be valid in some simulations involving intrusions. Similarly, the

stiffness characteristics of the knee bolsters are assumed to remain the same even in the presence

of dash intrusions.

The implementation of the EFP framework to develop MADYMO occupant environment models for frontal crash fleet simulations is summarized in Figure 3-41.

Figure 3-41. Occupant Modeling Framework for Frontal Fleet Crash Simulations

Occupant models were developed for the four partner vehicles (Taurus, Yaris, Explorer,

Silverado) using generic restraint systems designed to meet current regulations and consumer

information testing, and subsequently verified to frontal crash test data following the EFP

occupant modeling framework shown in Figure 3-40 (R. R. Samaha, P. Prasad, et al.,

Development and Validation of a Toyota Yaris MADYMO Frontal Occupant Model, NCAC

Report 2013-W-004 2013) (R. R. Samaha, P. Prasad, et al., Development and Validation of a

2001 Ford Taurus MADYMO Frontal Occupant Model, NCAC Report 2013-W-001 2013) (R. R.

Samaha, et al. 2013) (R. R. Samaha, et al. 2014). The available crash tests used in Part I for frontal occupant model development for the four partner vehicles are presented in Table 3-37.

Table 3-37. Available Frontal Crash Tests for Partner Vehicles Occupant Model Development

Crash Test, Dummy Size Yaris Taurus Explorer Silverado 35 mph rigid wall, 50th     35 mph rigid wall, 5th *  30 mph rigid wall, 50th  30 mph rigid wall, 5th   40 mph ODB, 50th    

th * 5 %tile was unbelted

3.16.1. Generic Occupant Restraint System Models

For the EFP frontal impact implementation, the following generic occupant environment models with modern restraints were obtained from restraint manufacturers: a MY 2009 pickup truck, a MY 2006 midsize SUV, a MY 2011 small car, and a MY 2007 midsize car. Additionally, the knee bolster stiffness functions and frontal interior plane geometry of a MY 2004 sedan were obtained from an automotive manufacturer and were used to enhance the foundation models.

• As the MY 2011 small car model includes a modern restraint system, it was used as the

foundation for both the Taurus and Yaris occupant models. The Taurus model was

supplemented by the knee bolster stiffness functions and frontal interior plane geometry

of a MY 2004 sedan.

• The MY 2006 SUV model was the foundation for Explorer, with the addition of the more

modern airbag model from the MY 2009 pickup model.

• The MY 2009 pickup truck restraint model was used as the foundation for the Silverado.

3.16.2. Vehicle Interior Geometry and Dummy Positioning

For vehicles in which interior geometry are available from the FE models, interior

components can be imported into MADYMO to guide the positioning of the model. The occupant

environment models for the Taurus and Yaris included the driver side toe pan geometry from the

corresponding FE model, which allowed the inclusion of prescribed structural motion to capture

localized intrusions in the toe pan. The Taurus and Yaris were positioned according to the test

report data, with no additional guidance from FE model geometry. The generic vehicle interior

components and dummy in the four occupant models were positioned according to the test report

data on clearances and angles. Measurements such as the steering wheel angle, seat back angle,

head-to-windshield, nose-to-rim, chest-to-dash, steering wheel-to-chest, rim-to-abdomen, left

knee-to-dash, right knee-to-dash, tibia angle, and knee-to-knee were considered in positioning the dummies. The dummies were positioned to match as many of these measurements as possible.

The models were also subject to visual inspection to ensure that the initial position for the simulation was physically reasonable and close to those available in the test data. As an example, the positions of the 50th percentile male dummy and 5th percentile female dummy for the Yaris

occupant model are shown in Figure 3-42 and Figure 3-43.

Figure 3-42. Initial position of 50th percentile Male dummy for Simulation in Yaris full frontal model

Figure 3-43. Initial position of 5th percentile Female Dummy for Simulation in Yaris full frontal model

Special attention should be paid to the knee-to-dash measurement in an effort to replicate the lower extremity behavior seen in the actual crash test and to accurately predict the femur injury risk. As an example, the lower extremity positioning of the Taurus 50th percentile occupant model is shown in Figure 3-44.

Figure 3-44. Lower Extremity Positioning of the 50th Percentile Dummy in the Taurus Occupant Model

In addition to proper positioning, it is also important to effectively couple the dummy to the vehicle interior, specifically in the interaction of the foot with the footrest and floor. In the Taurus occupant model, the foot was not well-coupled to the floor, causing the heel to elastically bounce off the floor upon impact. To achieve tighter coupling of the foot to the floor, a “foot stop” was added to the floor, acting as a heel rest for the right foot Figure 3-45. The addition of this foot stop allowed for more realistic lower extremity kinematics and closer correlation to test data.

Figure 3-45. Addition of Foot Stop to Taurus Occupant Model

3.16.3. Airbag and Pretensioner Firing Time Strategies

In consultation with an industry expert, a general vehicle design guideline, verified with crash test measurements, was used to establish an airbag firing strategy. The vehicle design guideline, known as the “5-30” rule, is based on free motion travel of an occupant computed from the compartment deceleration pulse as follows: fire the airbag and pretensioner 30 ms before the occupant reaches 5” of free motion travel, with an earliest firing time limit of 14 ms. This scenario allows the airbag to be inflated and in place to restrain the occupant. This guideline defines the time at which the airbag begins to generate gas. While the fill time of the airbag depends on the volume of the bag, this scenario should allow the airbag to be inflated and in place to restrain the occupant. The airbags in the models are not pre-inflated and the bag inflation time is dictated by the mass inflow rates of the inflators. Therefore, the bag/occupant interaction time is predicted in the models and not assumed. In the simulations, the airbag and pretensioner are triggered at the same time. The crash test data is checked to verify if a vehicle conforms to the design guideline, in particular if the occupant crash pulse and the shoulder belt data are available.

For some vehicles, the 5-30 rule may not be achievable and the firing time is later, thus allowing

more occupant excursion. For these cases, the test data, specifically the belt pretensioner firing

and compartment pulse, are used to determine a rule specific to that vehicle (R. R. Samaha, P.

Prasad and D. Marzougui, et al. August 2014).

3.16.4. Restraint System Fine Tuning

Once the dummy is reasonably positioned within the vehicle, the simulation is run and the

restraints are modified through an iterative process. Because the restraint models are generic, it is

necessary to fine tune the restraint characteristics within realistic value ranges to achieve a better

match to the crash test data. The baseline occupant models were developed with the ability to

change belt restraint geometry, pretensioners, load limiters, and airbag size, mass-flow rate, and

vent parameters. For each vehicle, the restraint system was fine-tuned through an iterative process

until the model output was a good match to the occupant accelerations in available crash tests (R.

R. Samaha, P. Prasad and D. Marzougui, et al. August 2014). The shoulder belt loads were

examined to determine pretensioner firing times, pretensioner loads, and the shoulder belt load

limiting characteristics (an example is shown in Figure 3-46). The retractor model parameters

such as slack, belt payout, pretensioner forces, and load limiting characteristics were adapted in

the model to obtain a good match with the test data.

Figure 3-46. Typical Shoulder Load from Crash Test

3.17. Verification of Vehicle and Occupant Models for Fleet Simulations

For verification of the vehicle and occupant models in support of the EFP frontal impact implementation, a series of simulations were designed to check model robustness and simulation trends in the modeled frontal crash configurations. The simulations for both the vehicle and occupant models were performed by NCAC support staff.

3.17.1. Vehicle Structure FEM Robustness Checks

As part of the partner vehicle FE model extended validations discussed in section 3.5.3, a series of large deformation impacts were simulated to check model stability in the most severe

conditions planned for EFP simulations. A centerline pole impact at 35 mph was selected for one

of the robustness runs, as it is a severe, high speed crash with large, localized deformation.

Additionally, models of both passenger cars, the Taurus and Yaris, were tested for robustness in

full engagement and 40% offset frontal impacts at 35 mph with the Chevrolet Silverado FE model. The results from these robustness simulations were used to further develop and stabilize the FE models as needed. The verified models provided viable representations in these large deformation crash events. As an example, the extent of post-crash deformation in the Yaris to

Silverado full engagement and 40% offset impacts is shown in Figure 3-47 and Figure 3-49. The

Yaris compartment acceleration (also called vehicle crash pulse) in these high deformation impacts is shown in Figure 3-48 and Figure 3-50.

Figure 3-47. Pre- and post-crash images of Yaris to Silverado full engagement impact

Figure 3-48. Compartment acceleration of Yaris in full frontal impact with Silverado

Figure 3-49. Pre- and post-crash images of Yaris to Silverado 40% offset frontal impact

Figure 3-50. Compartment acceleration of Yaris in 40% offset impact with Silverado

3.17.2. Vehicle and Occupant Additional Checks and Trends Analysis

The partner vehicle FE models were also run at low and high speeds within a given crash configuration to verify that the vehicle responses were consistent and in the physical realm. The following impacts were simulated:

• NHTSA New Car Assessment Program (NCAP) rigid wall at 25 mph and 35 mph • IIHS Offset Deformable Barrier (ODB) at 25 mph and 40 mph • Centerline pole at 25 mph and 35 mph

Overall, the simulations executed without error for the four partner vehicle FE models and the results reflected the expected trends and consistency with varying parameters. As an example, the extent of post-crash deformation for the Yaris for the trends analysis impact simulations is shown in Table 3-38.

100

Table 3-38. Post-crash Images of the Yaris for the Varying Speed Simulation Trend Analysis

25 mph (40 km/h) 35 mph (56 km/h) 40 mph (64 km/h) for ODB

Full frontal

IIHS ODB

ole

Centerline p Centerline

The Yaris vehicle crash pulse overlays in the trends analyses simulations are shown in Figure

3-51, Figure 3-52, and Figure 3-53.

Figure 3-51. Yaris compartment accelerations for NCAP frontal verification simulations

101

Figure 3-52. Yaris compartment accelerations for ODB frontal verification simulations

Figure 3-53. Yaris compartment accelerations in Centerline pole verification simulations

For the occupant model robustness checks, outputs from the vehicle FE trend analysis simulations were used to drive the occupant models, as outlined in Part II of the EFP occupant model development framework discussed in section 3.15 and presented in Figure 3-41. The occupant simulation outputs for the head, neck, chest, and knee-thigh-hip (KTH) body regions were used to compute risk of serious injury (ASI3+) by body region for both midsize (50th percentile) male and small (5th percentile) female drivers, using the biomechanics injury risk functions presented in section 3.13. The simulated occupant injury risk trends for both occupant sizes were generally consistent and reasonable. The combined serious injury risks for the head, 102

neck, chest, and femur were subsequently computed for simulated impact conditions and the results are shown in Figure 3-54, Figure 3-55, Figure 3-56, and Figure 3-57.

Based on the Combined Injury Risks (CIRs), one would expect 4-star to 5-star ratings for all four vehicles in a frontal impact rating system4. All the vehicles would have met or exceeded the requirements of the regulatory tests, thus demonstrating the performance of the modern restraints used in the occupant models. In general, the KTH risks contributed substantially to the CIR in the centerline pole tests. As noted earlier, the government 2011 NCAP testing program combines serious injury risks (AIS3+) for the head, chest, and neck body regions with AIS2+ injury risk for the KTH region, while in this research, the AIS3+ injury risk was combined for all the body

regions instead to ensure consistency relative to threat to life of the predicted injury risks. The

various simulations indicate that the small sized occupants represented by the 5th percentile female dummy in the forward most seating position is generally at a higher risk of injury than the average adult male represented by the 50th percentile male dummy.

Figure 3-54. Taurus Occupant Model Simulation verification and robustness trends

4 In the current NCAP frontal testing, 5 Star, 4 Stars, 3 Stars, 2 Stars, and 1 Star correspond to the following combined injury risks ranges, P ≤ 0.1, 0.1 ≤ P < 0.15, 0.15 ≤ P < 0.2, 0.2 ≤ P < 0.4, and P ≥ 0.4 (NHTSA July 2008). 103

Figure 3-55. Yaris Occupant Model Simulation verification and robustness trends

Figure 3-56. Explorer Occupant Model Simulation verification and robustness trends

104

Figure 3-57. Silverado Occupant Model Simulation verification and robustness trends

Overall, the results demonstrate that the developed occupant models would effectively predict probabilities of serious injuries to the head, chest, neck, and femur body regions for restrained

midsize male and small female drivers in frontal crashes.

3.18. Summary of Modeling Process for EFP Frontal Impact Implementation

Crash pulses and intrusions are output from the FE vehicle structure simulations for target

and partner vehicles. The simulation impact conditions are presented in the EFP matrices for

single- and two-vehicle crash simulations, shown in Table 3-24 and Table 3-25. The FE crash pulses and intrusions are incorporated in the MADYMO model to drive the occupant simulations.

Two occupant simulations are run for each vehicle structure simulation, one for the 50th percentile male and one for the 5th percentile female driver, using Hybrid III multibody models for both sizes of occupants (TASS 2009) (TASS 2009). The occupant responses output from the

MADYMO simulations are input to biomechanics injury risk functions to predict probabilities of serious injuries by body region in the target and partner vehicles for restrained drivers. The individual injury risks by body regions are combined to estimate a combined injury for the target

105

and partner vehicle drivers, the sum of which provides a measure of societal risk for the crash incident represented by the individual impact simulation. The FE vehicle simulation outputs are

also used to extract crash structural response attributes such as dynamic interior intrusions,

vehicle crash pulse index5 (VPI), which is a measure of crash pulse severity, and internal energies absorbed by substructures.

5 ISO/TR 12353-3:2013, Road vehicles -- Traffic accident analysis -- Part 3: Guidelines for the interpretation of recorded crash pulse data to determine impact severity. 106

Chapter 4. EFP Computations, Results, and Discussions

In this Chapter, a proof-of-concept application of EFP frontal impact implementation is first presented in which driver societal risk is assessed for three midsize target vehicles developed at the NCAC: a baseline and two simple design variants. The EFP injury risk computations and detailed results are methodically illustrated through the proof-of-concept application. Next, an application of EFP to compute and assess the change in driver societal injury risk between three vehicles is presented: a midsize passenger car and two light-weighted design concepts of a midsize CUV. These vehicle designs were developed in recent projects by the California Air

Resources Board (CARB), Environmental Protection Agency (EPA), and National Highway

Traffic Safety Administration (NHTSA) as part of the Corporate Average Fuel Economy (CAFE) research efforts for NHTSA (FEV 2012) (Singh, et al. 2012) (Lotus Engineering Inc. 2012).

Lastly, to provide insight into future light-weighted fleet safety interactions, frontal EFP was applied in a case study of a lightweight versus baseline fleet, each composed of two vehicle segments: a midsize passenger car and a midsize CUV.

Further analyses and results demonstrating EFP capabilities are also included within the applications presented in this Chapter:

• Use of frontal EFP to assess suitability of injury risk functions for a chest injuries case

study.

• Use of EFP in several scenario analyses to assess the safety effects of fleet composition

changes.

107

4.1. Proof-of-Concept EFP Application

As a proof-of-concept, EFP was applied to assess frontal driver societal risk for three midsize target vehicles:

• The Taurus fleet partner FE vehicle model as a target baseline vehicle

• Two simple design variants of the Taurus as “surrogate” new designs of the baseline: one

25% lighter, one stiffer

The two design variants of the Taurus vehicle, shown in Table 4-1, were developed at the

NCAC and are not meant to be realistic designs (R. R. Samaha, P. Prasad and D. Marzougui, et

al. August 2014). They were developed to evaluate if EFP could detect changes in occupant risk

related to vehicle design modifications. These designs were also selected to allow for independent

examination of safety effects related to changing vehicle weight and stiffness. In the lightweight

Taurus design (Taurus_LW), a simple light-weighting strategy was implemented by reducing the density of all steel parts in the Taurus by approximately one-third, including a reduction of 100 kg from the engine (powertrain components). This design variant is a surrogate of a strategy where the vehicle has been light-weighted while the front end stiffness remains the same. For the stiffer Taurus design (Taurus_ST), all of the steel parts were replaced with a dual phase Ultra

High Strength Steel (DP500 UHSS), except the engine and transmission, as compared to the baseline. The Taurus_ST design is a surrogate of a strategy where the weight of the vehicle has not changed while the front end stiffness has increased.

Table 4-1. Material Properties of Steel in Taurus FE Models

Steel Properties FE Model FE Model Elastic Yield Density weight Design Modulus Strength 3 (kg/lbs) (kg/m ) (GPa) (Mpa) Baseline 7850 210 140-400 1515/3339 Taurus_LW 5233 210 140-400 1138/2508 Taurus_ST 7850 210 500 1515/3339 108

The EFP single- and two-vehicle finite element structural simulations and corresponding occupant simulations (shown in Table 3-24 and Table 3-25) were performed by NCAC support staff for the three Taurus target vehicles. The MPPDYNA_971 R46 version of LS-DYNA was

used to run the vehicle finite element simulations. The R7.37 version of the MADYMO solver was used to run the occupant rigid body simulation.

4.2. Occupant Responses and Injury Risk Computation

Spreadsheet templates were designed for selected occupant responses (output by the

MADYMO simulations) for both occupant sizes, across simulated impact speeds and impact configurations. The responses were checked for consistencies and trends. The corresponding time histories were reviewed and analyzed, as needed. As examples, Table 4-2 and Table 4-3 present the occupant responses from the Taurus baseline single-vehicle full engagement impact simulations for the 50th percentile male and 5th percentile female driver dummies. The templates incorporated the selected injury risk functions and Combined Injury Risk (CIR) computations in a setup amenable for future upgrades of the risk functions. Note that both femur bending (i.e., lateral, My) and resultant moments were included to support future research in developing a more

relevant injury measure for the Knee-Thigh-Hip (KTH) complex body region.

6 LS-DYNA- Version: mpp971sR4.2.1, Revision:53450, Platform: Intel MPI 3.1 Xeon64 , OS Level: Linux Red Hat 4upd4, Precision: Single precision (I4R4) 7 MADYMO- Version: R7.3, Platform: Linux26-x86_64, 4 processors; Hybrid III 50th and 5th dummies release date 2009/05/19. 109

Table 4-2. Example Occupant Responses and Computed Injury Risks for 50th %tile Male Dummy

Taurus Baseline- Full Engagement Frontal, HIII 50th %ile Dummy Dummy Injury Quantity/Formula 15 mph 25 mph 30 mph 35 mph Measurement

HIC15 50 124 146 169

Neck Tension (T) Upper Neck Fz Maximum 849 1264 1303 1348

Femur Left moment (My) Maximum Moment 26 64 58 62 (Nm) Femur Right moment Maximum Moment 71 81 93 108 (My) (Nm) Femur Left Moment Maximum Moment 75 91 118 126 (Resultant) Femur Right Moment Maximum Moment 77 92 105 117 (Resultant)

Pelvis Acceleration (g) Max Resultant Acceleration 29 43 53 62

Chest Deflection (mm) Maximum deflection 22 25 26 26

Chest Acceleration (g) Maximum acceleration 28 35 39 41

Femur Load - Left (N) Maximum Compressive force Fz 417 926 1440 1927

Femur Load - Right (N) Maximum Compressive force Fz 1696 3112 4098 5273

HIC15 Risk (%) NORMDIST(LN(L3),7.45231,0.73998,1) 0.0% 0.0% 0.0% 0.1%

Chest Deflection (%) 1/(1+EXP(12.597-0.05861*35-1.568*((L17)^0.4612))) 2% 3% 3% 3%

Femur Max (%) 1/(1+EXP(4.9795-0.326*maxFemur /1000)) -1/(1+EXP(4.9795)) 0.5% 1.2% 1.9% 3.0%

Neck Tension (T)(%) 1/(1+EXP(10.9745-2.375*L5/1000)) 0.0% 0.0% 0.0% 0.0%

Combined Injury Risk (%) (1-(1-D21)*(1-D22)*(1-D23)*(1-D24)) 2.3% 3.8% 4.8% 6.0%

110

Table 4-3. Example Occupant Responses and Computed Injury Risks for 5th %tile Female Dummy

Taurus Baseline- Full Engagement Frontal, HIII 5th %ile Dummy Dummy Injury Quantity/Formula 15 mph 20 mph 25 mph 30 mph 35 mph Measurement

HIC15 171 176 184 191 190

Neck Tension (T) Upper Neck Fz Maximum 1399 1395 1415 1438 1412

Femur Left moment (My) Maximum Moment 16 13 14 15 14 (Nm) Femur Right moment Maximum Moment 37 36 52 53 64 (My) (Nm) Femur Left Moment Maximum Moment 68 71 74 68 66 (Resultant) Femur Right Moment Maximum Moment 54 55 56 71 68 (Resultant)

Pelvis Acceleration (g) Maximum Resultant Acceleration 37 35 35 35 33

Chest Deflection (mm) Maximum deflection 23 24 24 25 26

Chest Acceleration (g) Maximum acceleration 30 33 35 38 42

Femur Load - Left (N) Maximum Compressive force Fz 392 264 403 488 514

Femur Load - Right (N) Maximum Compressive force Fz 1834 2035 2215 2389 2556

HIC15 Risk (%) NORMDIST(LN(D3),7.45231,0.73998,1) 0.1% 0.1% 0.1% 0.1% 0.1%

Chest Deflection (%) 1/(1+EXP(12.597-0.05861*35-1.568*((D17/0.817)^0.4612))) 4% 4% 4% 5% 6%

Femur Max (%) 1/(1+EXP(4.9795-0.47941*maxFemur/1000)) -1/(1+EXP(4.9795)) 0.9% 1.1% 1.3% 1.4% 1.6%

Neck Tension (T)(%) 1/(1+EXP(10.958-3.77*D5/1000)) 0.3% 0.3% 0.4% 0.4% 0.4%

Combined Injury Risk (%) (1-(1-D21)*(1-D22)*(1-D23)*(1-D24)) 5.1% 5.8% 6.0% 6.8% 7.6%

The CIR was computed for the driver in each target vehicle, i.e., the Taurus baseline,

Taurus_LW, and Taurus_ST in single- and two-vehicle crashes, by impact speed and crash configuration per the EFP simulation matrices, as illustrated in the overview schematic in Figure

4-1.

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Each Impact simulated at five speeds for 50th male & 5th tile female dummies

Target Full Frontal CIR (Combined Injury Risk Head, Neck, Chest & Knee-Thigh-Hip) Target Offset Frontal • Target Centerline Pole • Configurations Single Vehicle (SV) Crash Crash (SV) Vehicle Single Target to Explorer Full • Target to Explorer Offset • • Target to Silverado Full Target CIR (Combined Injury Risk Head, Neck, Chest & Knee-Thigh-Hip) Target to Silverado offset Partner CIR (Combined Injury Risk Head, Neck, Chest & Knee-Thigh-Hip) Target to Yaris Full • Configurations • Target to Yaris Offset •

Vehicle to Vehicle (VTV) Crash Crash (VTV) Vehicle to Vehicle • Target to Taurus Full

Target to Taurus Offset

Figure 4-1. CIR Computation for Each Simulated Crash Incident for a Target Vehicle

The CIR was tabulated in linked spreadsheets for the driver of the target vehicle in single- and two-vehicle crashes and for the driver of the partner vehicles in the corresponding two- vehicle crashes. As examples, Table 4-4 and Table 4-5 present the occupant responses and computed injury risks for the 50th percentile male driver dummy in the Taurus baseline target.

The results for 5th percentile female driver in the Taurus baseline and for occupants in the

Taurus_LW and Taurus_ST crash simulations are provided in Appendix C.

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Table 4-4. Example Target Occupant Reponses and Computed Injury Risk Data in Single-Vehicle Crash Simulations

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Table 4-5. Example Target Occupant Reponses and Computed Injury Risk Data in Two-Vehicle Crash Simulations

TAURUS BASELINE TARGET VEHICLE OCCUPANT HIII 50th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur Max HIC15 Risk Femur Max Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) (N) (%) (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 53 22 1567 810 0.0% 1.8% 0.5% 0.0% 2.2% 1.8% 20 107 25 2463 1158 0.0% 2.6% 0.8% 0.0% 3.4% 2.6% Explorer Full 25 138 26 4758 1266 0.0% 2.9% 2.5% 0.0% 5.4% 3.0% 30 194 27 6027 1479 0.2% 3.3% 4.0% 0.1% 7.4% 3.5% 35 281 27 5375 1606 0.7% 3.3% 3.1% 0.1% 7.1% 4.1% 15 21 20 268 597 0.0% 1.3% 0.1% 0.0% 1.4% 1.3% 20 45 22 695 885 0.0% 1.8% 0.2% 0.0% 1.9% 1.8% Explorer Offset 25 102 24 2235 1205 0.0% 2.3% 0.7% 0.0% 3.0% 2.3% 30 132 27 3017 1448 0.0% 3.3% 1.1% 0.1% 4.5% 3.4% 35 265 28 3985 1833 0.6% 3.7% 1.8% 0.1% 6.1% 4.4% 15 35 21 1396 691 0.0% 1.5% 0.4% 0.0% 1.9% 1.5% 20 104 25 3070 1130 0.0% 2.6% 1.2% 0.0% 3.7% 2.6% Silverado Full 25 124 26 5256 1205 0.0% 2.9% 3.0% 0.0% 5.9% 3.0% 30 164 27 7590 1401 0.1% 3.3% 6.9% 0.0% 10.1% 3.4% 35 220 27 8914 1532 0.3% 3.3% 10.5% 0.1% 13.7% 3.6% 15 36 21 373 827 0.0% 1.5% 0.1% 0.0% 1.6% 1.5% 20 67 24 983 1057 0.0% 2.3% 0.3% 0.0% 2.6% 2.3% Silverado Offset 25 113 24 1628 1133 0.0% 2.3% 0.5% 0.0% 2.8% 2.3% 30 144 26 2485 1365 0.0% 2.9% 0.8% 0.0% 3.8% 3.0% 35 169 27 3373 1488 0.1% 3.3% 1.3% 0.1% 4.7% 3.4% 15 30 20 1110 658 0.0% 1.3% 0.3% 0.0% 1.6% 1.3% 20 59 23 1998 925 0.0% 2.0% 0.6% 0.0% 2.6% 2.0% Yaris Full 25 110 25 3577 1180 0.0% 2.6% 1.5% 0.0% 4.1% 2.6% 30 132 25 4258 1291 0.0% 2.6% 2.0% 0.0% 4.6% 2.7% 35 162 27 5655 1276 0.1% 3.3% 3.5% 0.0% 6.8% 3.4% 15 5 11 219 315 0.0% 0.3% 0.1% 0.0% 0.4% 0.3% 20 9 15 259 356 0.0% 0.6% 0.1% 0.0% 0.7% 0.6% Yaris Offset 25 30 21 441 703 0.0% 1.5% 0.1% 0.0% 1.6% 1.5% 30 82 22 1148 976 0.0% 1.8% 0.3% 0.0% 2.1% 1.8% 35 152 24 1853 1277 0.1% 2.3% 0.6% 0.0% 2.9% 2.4% 15 27 20 1078 605 0.0% 1.3% 0.3% 0.0% 1.6% 1.3% 20 66 23 1918 920 0.0% 2.0% 0.6% 0.0% 2.6% 2.0% Taurus Full 25 111 24 2827 1202 0.0% 2.3% 1.0% 0.0% 3.3% 2.3% 30 132 26 4334 1254 0.0% 2.9% 2.1% 0.0% 5.0% 3.0% 35 163 26 5124 1349 0.1% 2.9% 2.8% 0.0% 5.8% 3.0% 15 12 17 254 429 0.0% 0.9% 0.1% 0.0% 0.9% 0.9% 20 21 19 224 624 0.0% 1.2% 0.1% 0.0% 1.2% 1.2% Taurus Offset 25 47 22 559 781 0.0% 1.8% 0.1% 0.0% 1.9% 1.8% 30 90 24 1634 1043 0.0% 2.3% 0.5% 0.0% 2.8% 2.3% 35 194 26 2471 1513 0.2% 2.9% 0.8% 0.1% 4.0% 3.1% 114

4.3. Societal Injury Risk Computation for a Target Vehicle

The societal injury risk for a target vehicle is computed by aggregating the injury risk for the target vehicle occupant in single-vehicle crash simulations and the sum of the risks of both target vehicle and partner vehicle occupants in two-vehicle crash simulations. This is illustrated in the schematic shown in Figure 4-2.

Figure 4-2. Overview of Societal Injury Risk Computation for a Target Vehicle, SV=Single-Vehicle and VTV=Two-Vehicle Crash Simulations

Computation of societal injury risk is based on Equation 3-3 with application of EFP real- world weighting factors, i.e., frequency of occurrence derived from NASS CDS, as presented in

Chapter 3. The approach for application of the weighting factors was formulated to allow safety insights by crash partner in a given crash configuration and overall by occupant size and crash events. Pertinent weighting factors were normalized to 100%, as presented later in this section, to predict injury risks that can be compared with the field, i.e., real-world data from NASS CDS.

The equation for societal risk computation in the EFP frontal impact implementation is presented below as Equation 4-1.

( ) = 푇 ( ) ( ) (4-1) 퐽�퐽𝐽� 퐾𝐾𝐾𝐾 퐿𝐿𝐿�퐿� 푀�푀푀푀𝑀푀 푁푁𝑁푁�푁 푃 푃푃�푃푃푃 푆푆�𝑓𝑓𝑓� 푣 ∑�=1 ∑�=1 ∑�=1 ∑�=0 ∑�=1 ∑�=1 ∑�=1 푤𝑗𝑗𝑗� 푣 ∗ 퐶𝐶𝑗𝑗𝑗� 푣

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The societal injury risk by crash incident for single-vehicle crash simulations is presented in

Table 4-6 for the Taurus target vehicles. The societal injury risk by crash incident for the two- vehicle crash simulations is presented in Table 4-7 for the Taurus target vehicles. The societal risk for a two-vehicle crash incident is the sum of driver risk in both the target (i.e., Taurus

Baseline or Taurus_LW or Taurus_ST) and partner vehicle (e.g., Explorer or Yaris). The elevated risk values in the higher speed impacts with the Silverado and Explorer Table 4-7 are due to the high femur loads in the Taurus targets vehicles.

Table 4-6. CIR in Single-Vehicle Crash Incidents for the Taurus Vehicle Targets

Taurus Baseline Taurus_LW Taurus_ST SV Crash Speed Taurus_BL Taurus_BL Taurus_LW Taurus_LW Taurus_ST Taurus_ST Configuration (mph) 50th %ile 5th %ile 50th %ile 5th %ile 50th %ile 5th %ile Male Female Male Female Male Female 15 2.3% 5.1% 3.2% 5.8% 4.3% 4.7% 20 3.1% 5.8% 4.6% 6.8% 6.8% 6.1% Full Frontal 25 3.8% 6.0% 5.5% 7.0% 7.8% 6.1% 30 4.8% 6.8% 6.2% 7.9% 9.4% 7.9% 35 6.0% 7.6% 8.3% 8.9% 17.3% 9.0%

20 1.1% 3.8% 1.4% 4.4% 1.2% 2.4% 25 1.4% 4.4% 1.7% 4.5% 1.6% 2.8% Offset Frontal 30 1.7% 5.5% 2.3% 7.2% 2.3% 5.0% 35 2.4% 5.4% 2.7% 7.0% 3.8% 7.5% 40 2.7% 5.3% 3.2% 6.6% 23.9% 7.7%

15 0.8% 2.7% 1.0% 2.8% 1.1% 2.2% 20 1.3% 3.5% 1.8% 4.6% 2.7% 4.3% Center Pole 25 2.4% 4.8% 3.1% 6.0% 4.1% 5.5% 30 3.9% 7.0% 4.2% 7.7% 6.3% 7.2% 35 5.3% 7.7% 5.5% 8.7% 10.2% 11.4%

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Table 4-7. CIR in Two-Vehicle Crash Incidents for the Taurus Vehicle Targets (sum of target and partner driver injury risk)

Taurus Baseline Taurus_LW Taurus_ST VTV Crash Speed Taurus_BL Taurus_BL Taurus_LW Taurus_LW Taurus_ST Taurus_ST Configuration (mph) 50th %ile 5th %ile 50th %ile 5th %ile 50th %ile 5th %ile Male Female Male Female Male Female 15 5.0% 9.1% 5.4% 10.1% 6.1% 8.3% 20 8.4% 10.5% 8.2% 11.2% 10.1% 10.2% Explorer Full 25 11.0% 13.0% 14.0% 14.2% 13.9% 14.2% 30 13.6% 16.1% 16.3% 19.9% 32.2% 16.7% 35 14.8% 18.5% 17.2% 22.6% 43.0% 21.0%

15 2.8% 8.3% 2.0% 7.0% 2.3% 6.0% 20 3.6% 10.3% 4.1% 10.7% 5.6% 10.5% Explorer Offset 25 5.5% 12.8% 5.6% 12.8% 8.2% 11.7% 30 9.0% 12.6% 8.4% 15.0% 12.0% 15.6% 35 11.9% 15.2% 15.8% 15.9% 30.4% 18.9%

15 3.8% 9.3% 4.3% 10.2% 5.4% 10.0% 20 7.2% 12.5% 7.9% 12.0% 9.1% 13.1% Silverado Full 25 10.5% 15.1% 14.6% 16.5% 19.2% 15.0% 30 15.7% 17.4% 40.0% 18.1% 21.6% 14.7% 35 19.6% 17.9% 38.9% 23.3% 29.4% 21.3%

15 3.2% 9.1% 3.4% 9.7% 4.8% 10.7% 20 4.6% 11.7% 4.9% 12.0% 7.8% 13.8% Silverado Offset 25 5.1% 13.3% 6.2% 12.9% 7.0% 13.4% 30 6.9% 14.8% 7.5% 15.9% 8.2% 12.1% 35 8.7% 17.4% 11.1% 19.1% 9.0% 14.3%

15 4.5% 10.6% 5.4% 10.9% 6.0% 12.1% 20 6.4% 13.9% 7.4% 13.8% 7.4% 13.6% Yaris Full 25 7.9% 18.0% 9.7% 18.7% 10.7% 17.4% 30 9.2% 18.4% 10.9% 19.4% 15.3% 19.0% 35 17.7% 21.1% 14.8% 22.1% 28.7% 19.2%

15 1.6% 3.9% 1.9% 4.0% 2.6% 3.6% 20 2.8% 6.3% 2.7% 5.6% 4.8% 8.7% Yaris Offset 25 5.5% 12.8% 5.7% 12.8% 6.8% 13.1% 30 8.1% 14.8% 7.1% 15.9% 11.6% 17.0% 35 12.8% 16.5% 13.3% 16.2% 14.2% 19.2%

15 3.2% 8.3% 3.9% 9.3% 3.5% 7.8% 20 5.2% 10.6% 6.2% 11.1% 5.2% 9.8% Taurus Full 25 6.6% 11.7% 8.2% 13.2% 8.8% 11.9% 30 10.0% 13.5% 10.6% 14.5% 13.2% 13.8% 35 11.6% 13.8% 14.1% 15.0% 18.7% 15.0%

15 1.8% 7.4% 2.5% 9.2% 2.0% 7.4% 20 2.4% 9.2% 3.0% 9.2% 3.4% 9.4% Taurus Offset 25 3.8% 10.0% 4.2% 10.9% 4.9% 11.3% 30 5.5% 10.7% 5.5% 13.0% 8.3% 14.2% 35 7.9% 13.6% 7.2% 13.7% 10.2% 17.7%

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To illustrate the computation of accumulated societal injury risk for a target vehicle, an

overview schematic is shown for single-vehicle crashes, as an example, in Figure 4-3.

Figure 4-3. Societal Injury Risk Computation in Single-Vehicles Crashes for a Target Vehicle

For both single- and two-vehicle crash simulations, the initial EFP weighting applied is by

BES for each crash configuration, i.e., using the factors in

Table 3-33 and

Table 3-34 for both occupant sizes. The BES weighting factors for PC ≥ 3106 lbs. were applied for the Taurus Baseline and Taurus_ST (FE model weight 3339 lbs., shown in Table 4-1).

The BES weighting factors for PC < 3106 lbs. were applied for the Taurus_LW (FE model

weight 2508 lbs.). As no simulations were performed at the low speed range of 0-11 mph where

low serious injuries occur (around 10%), the corresponding weighting factors were not used in

the analysis. The societal risk, accumulated over speed, by crash configuration and occupant size,

is presented in Table 4-8 for single-vehicle crashes and in Table 4-9 and Table 4-10 for two-

vehicle crashes for the Taurus target vehicles.

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Table 4-8. Societal Risk in Single-Vehicle Crashes by Crash Configuration, crash Partner and Occupant Size for the Taurus Targets

Full Frontal Full Frontal Offset Frontal Offset Frontal Center Pole Center Pole Target 50th male 5th female 50th male 5th female 50th male 5th female Taurus BL 1.33% 2.64% 0.64% 2.06% 1.11% 2.50% Taurus_LW 1.86% 2.80% 0.85% 2.47% 1.45% 3.40% Taurus_ST 2.70% 2.60% 1.14% 1.52% 1.94% 2.74%

Table 4-9. Societal Risk in Two-Vehicle Crashes by Crash Configuration, Light Truck Crash Partner and Occupant Size for the Taurus Targets

SUV offset Pickup Full Pickup Offset Pickup Offset Target SUV Full 50th SUV Full 5th SUV offset 5th Pickup Full 5th 50th 50th 50th 5th Taurus BL 3.36% 5.04% 1.02% 2.75% 2.84% 5.45% 1.12% 3.03% Taurus_LW 4.05% 6.23% 1.12% 3.11% 4.02% 6.48% 1.51% 3.91% Taurus_ST 4.40% 4.81% 1.15% 2.26% 4.08% 5.72% 1.67% 3.47%

Table 4-10. Societal Risk in Two-Vehicle Crashes by Crash Configuration, PC Crash Partner and Occupant Size for the Taurus Targets

Small PC Full Small PC Full Small PC Small PC Mid-Large PC Mid-Large PC Mid-Large PC Mid-Large PC Target 50th 5th Offset 50th Offset 5th Full 50th Full 5th Offset 50th Offset 5th Taurus BL 2.78% 6.19% 0.72% 1.59% 2.14% 4.69% 0.68% 2.42% Taurus_LW 3.66% 7.09% 0.95% 1.96% 2.86% 5.77% 1.03% 3.42% Taurus_ST 3.62% 6.59% 1.10% 1.66% 2.39% 4.44% 0.81% 2.50%

For single-vehicle crash simulations, the second EFP weighting applied is for crash configuration. For example, the entries for the column “Total Risk 5th Female” shown in Table

4-11 are the weighted sums of the injury risks for the 5th female in the three crash configurations with weighting factors of the crash configuration distribution. The applied factors are normalized to a total of 100% (shown in Table 4-12, based on population distribution in Table 3-27).

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Table 4-11. Accumulated Societal Risk in Single-Vehicle Crashes: Overall & by Occupant Size

Overall Risk Total Risk Total Risk Target in SV 50th male 5th female Taurus BL 0.30% 0.22% 0.52% Taurus_LW 0.39% 0.30% 0.64% Taurus_ST 0.44% 0.42% 0.51%

Table 4-12. Frontal EFP Crash Configuration Weighting Factors

NASS/CDS Exposure all Full of 100% Offset Frontal Between Rail Engagement Single vehicle 6.02% 6.15% 9.42% Two Vehicle 44.50% 33.90% 0%

For two-vehicle crash simulations, the second EFP weighting applied is for crash

configuration factors modulated by crash exposure of vehicle class airings. For example, the entries for the column title “Total Risk 5th Female” shown in Table 4-13 are the sums of the

injury risks for the 5th female in the eight crash configurations, two for each crash partner as

shown in Table 4-9 and Table 4-10, and weighted by the modulated crash configuration factors

presented in Table 4-14. The crash exposure pairing factors, normalized to 100% passenger car

targets are presented in Table 4-15.

Table 4-13. Accumulated Societal Risk in Two-Vehicle Crashes: Overall & by Occupant Size

Overall Risk Total Risk Total Risk Target in VTV 50th male 5th female Taurus BL 1.97% 1.55% 3.24% Taurus_LW 2.45% 1.98% 3.88% Taurus_ST 2.36% 2.05% 3.29%

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Table 4-14. Weighting Factors for Crash Configuration modulated by Vehicle Class Crash Exposure for Two-Vehicle Crash Simulation for PC Targets

Two-Vehicle Crash Exposure (Target- PC ≥3106 lbs.) Small PC Mid-Large PC Mid-Large PC SUV Full SUV offset Pickup Full Pickup Offset Small PC Full Offset Full Offset 10.7% 8.14% 10.7% 8.14% 15.4% 11.8% 7.7% 5.9% Two-Vehicle Crash Exposure (Target- PC <3106 lbs.) Small PC Mid-Large PC Mid-Large PC SUV Full SUV offset Pickup Full Pickup Offset Small PC Full Offset Full Offset 10.7% 8.1% 10.7% 8.14% 7.7% 5.9% 15.4% 11.8%

Table 4-15. Vehicle Crash Pairings – Normalized to 100% PC Target Class

Vehicle Pairings (≥3106lbs.)

HC/LT (SUV) HC/HT(pickup) HC/LC HC/HC 24.0% 24.0% 34.7% 17.3% Vehicle Pairings (<3106 lbs.)

LC/LT (SUV) LC/HT (pickup) LC/LC LC/HC

24.0% 24.0% 17.3% 34.7%

The third and last weighting factor applied for both single- and two-vehicle crashes is the

75% versus 25% relative distribution of the 50th percentile male and 5th percentile female in the crash-involved driver population, as derived in section 3.8. The entries in the “Overall Risk”

columns in Table 4-11 and Table 4-13 are the sums of the total risk for the two occupant sizes

weighted by their relative distributions for both single- and two-vehicle crash simulations. An

example schematic for societal risk computation is shown in Figure 4-4. The societal risks for

each of the three target vehicles as predicted by EFP are presented in Table 4-16.

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Figure 4-4. Taurus Baseline Societal Injury Risk Computation

Table 4-16. EFP Computed Societal Risk for Taurus Targets

Taurus Target Vehicle Taurus_LW Taurus_ST Baseline

Weight (lbs) 3339 2508 3339 mass reduction 831 % mass reduction 25% 0% Societal Risk 2.27% 2.84% 2.80% % Risk Increase 25% 23%

4.4. EFP Proof-of-Concept Application Results

The baseline Taurus and two simple design variants were subjected to three configurations of single-vehicle crashes and two configurations of two-vehicle crashes against four vehicles selected to represent the composition of the existing car and LTV fleets in the U.S. EFP societal injury risk for the three target vehicles was computed by aggregating the injury risk for the target vehicle occupant in single-vehicle crash simulations and the sum of the risks of both target vehicle and partner vehicle occupants in two-vehicle crash simulations. The four surrogate vehicles selected to represent the current fleet were a heavy car, the Taurus; a light car, the Yaris; a heavy light truck, the Silverado; and a light truck, the Explorer, as presented in Chapter 3. In

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Taurus_LW, the mass of the baseline Taurus was reduced by 25% (831 lbs.) strictly due to material substitution. In Taurus_ST, stiffness was increased by replacing the regular steel with high strength steel while maintaining the mass of the baseline Taurus. The overall results are as follows:

• For the two simple design variants of the baseline Taurus, the lighter Taurus_LW and the

stiffer Taurus_ST, EFP predicted an increase of 25% and 23% in overall societal injury

risk when compared to the baseline, i.e., there was an increase in the total risk to its own

occupant and to the occupants of the partner vehicles (Table 4-16). The majority of the

risk, around 85%, is from the simulations of two-vehicle crashes as compared with

single-vehicle crashes.

• In single-vehicle crashes, Taurus_ST consistently has elevated injury risk for the 50th

percentile male driver when compared to Taurus_LW and the baseline; while

Taurus_LW has higher risks than both Taurus_ST and the baseline for the 5th percentile

female driver (Table 4-8). The injury risk for the small size female driver is substantially

higher than that of the midsize male driver for all three Taurus targets in the three

configurations of single-vehicle crashes. Injury risks for the center pole and full

engagement frontal crash configurations are higher than the offset configuration overall.

• Generally, in two-vehicle crashes, the societal risks in the impacts with the light trucks

are higher than those in impacts with the passenger cars. The injury risk for the small

size female driver is also substantially higher than that of the midsize male for all three

Taurus targets. Both simple design variants have increased injury relative to the baseline

across the majority of impacts with all crash partners for both sizes of occupants. The

Taurus_LW has mostly increased risk as compared with the Taurus_ST for both sizes of

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occupants. Overall, the injury risks for the full engagement frontal configuration are

higher than the risks for the offset configuration.

In the following subsections, details are presented for the combined and societal injury risks for the three Taurus targets. To assist in the interpretation and evaluation of trends, the results are

broken down by speed and crash mode.

4.4.1. Taurus Self-Protection Occupant Risk

4.4.1.1. Single-Vehicle Crashes

The Taurus single-vehicle driver injury risks show that safety in single-vehicle crashes will

be reduced in both the lighter (Taurus_LW) and stiffer (Taurus_ST) vehicles relative to the

baseline across all impact velocities, as shown in Figure 4-5. All drivers represented by the 5th

percentile to 50th percentile dummies (close to 90%) will have higher injury risk relative to the

baseline in the lighter and stiffer vehicles. In general, for all design options, the 5th percentile

female driver incurred higher risk than the 50th percentile male driver.

Figure 4-5. Taurus Driver Combined Injury Risk CIR in Single-Vehicle Crashes

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Particularly interesting in these single-vehicle simulations is that the lighter vehicle,

Taurus_LW, has the same structural stiffness as the baseline vehicle. Being lighter, there is less

energy to absorb in these collisions, and being equal in stiffness to the baseline, there is enough

structure to absorb the energy. In spite of the two factors in its favor compared to the baseline

vehicle, the injury risks are higher in the lighter mass vehicle across all speeds and occupant

sizes. This is due to higher accelerations of the passenger compartment, subjecting the occupants

to higher inertial loading.

Head strike through the airbag in the offset crash at 40 mph was observed for the 50th

percentile male dummy. The hard contact with the steering wheel caused a sharp spike in the

head acceleration, shown in Figure 4-6, causing the head injury risk to dominate the combined injury risk for this crash incident.

Figure 4-6. Head Resultant Acceleration for 50th Percentile Dummy in Taurus_ST Offset Frontal Crash

The 5th percentile dummy had an elevated chest injury risk compared to the 50th percentile

dummy. The restraints are designed to avoid strike-through for the heavier occupants at high

speeds, and thus have high load limiter forces. Without an adaptive load limiter, the smaller and

lighter occupants have higher chest injury risks.

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4.4.1.2. Vehicle-to-Vehicle Full Engagement Self-Protection

The combined injury risks for the driver in the three Taurus target vehicles in full engagement

frontal impacts with the four partner vehicles are shown in Figure 4-7. The CIR without the femur

risk is shown in Figure 4-8. The risk in the baseline Taurus is lower than both the lighter and

stiffer vehicles for all driver occupants in collisions with all partner vehicles. Figure 4-7 shows a substantial increase in risk observed when the target vehicle was struck by an Explorer or

Silverado, which can be attributed to high femur loads, as this sudden increase is not seen in

Figure 4-8. These high femur loads may not be representative of the real world. In the occupant models, the knee bolster stiffness is generic and the bottoming out stiffness may not be realistic for these vehicles. The trends are generally the same in both figures—the baseline risk is lower than those in the modified vehicles, and the 5th percentile dummy risks are higher than those of

the 50th percentile dummy.

Figure 4-7. Taurus Driver Combined Injury Risk for Two-Vehicle Full Engagement Crashes

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Figure 4-8. Taurus Driver Combined Injury Risk without Femur for Two-Vehicle Full Engagement Crashes

4.4.1.3. Vehicle-to-Vehicle Offset Self-Protection

The combined injury risks for the driver in the three Taurus target vehicles in offset frontal

impacts with the four partner vehicles are shown in Figure 4-9. Similar to the full engagement crashes, it is shown that the self-protection in the lighter and stiffer vehicles is reduced relative to the baseline vehicle against all four partner vehicles. The combined injury risk for the 35 mph impact with the Explorer is driven by the head injury, as head strike-through is observed in the simulation.

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Figure 4-9. Taurus Driver Combined Injury Risk in Two-Vehicle Offset Frontal Crashes

4.4.1.4. Summary of Taurus Self-Protection Results

Overall, both the lighter (Taurus_LW) and stiffer (Taurus_ST) Taurus vehicle design options exhibit good protection in regulatory and consumer information tests (i.e., NCAP and IIHS) as shown in the predicted injury risks in the full engagement and offset frontal single-vehicle crash simulations. However, they both exhibit higher injury risks than the baseline vehicle in all single- and two-vehicle crashes across the range of impact speeds.

4.4.2. Taurus Partner Injury Risk in Vehicle-to-Vehicle Crashes

Figure 4-10 shows the combined injury risks in the baseline Taurus and Yaris partner vehicles in the full engagement and offset configurations when impacted by the three Taurus target vehicles.

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Figure 4-10. Combined Injury Risk CIR for PC Partner Vehicle Impacted by Taurus Vehicles

Figure 4-11. Combined Injury Risk CIR for LT Partner Vehicle Impacted by Taurus Vehicles

The injury risks in the Yaris and baseline Taurus partner are somewhat lower when impacted by the Taurus_LW, in both full engagement and offset crashes relative to being impacted by the baseline Taurus. The Taurus_ST model, being a stiffer vehicle than the baseline Taurus and

Taurus_LW, causes greater injury risk in the partner vehicles than the baseline Taurus. These 129

trends are similar for the 50th percentile male driver and the 5th percentile female driver. Overall, the injury risks are lower in the light truck Explorer and Silverado partner vehicles as compared with the PC partner vehicles. The Explorer has a stiffer front end relative to the Taurus and

Silverado. It has the lowest partner injury risk and little differentiation for the risks sustained by the 50th percentile male and 5th percentile female drivers. With exception of the Explorer, the

injury risk for the small size female driver is higher than that of the midsize male driver across the range of impact speeds.

4.4.3. Limitations: Based on Proof-of-Concept Implementation

Limitations of the current implementation of EFP to frontal impacts include the lack of certain details in both vehicle and occupant models, the lack of availability of the custom components for the restraint systems in the occupant models, and fidelity of state-of-the-art injury risk functions.

• For example, the vehicle structural models did not include a model of the steering column

linkage system, so corresponding intrusions were not input to the occupant models.

Intrusions of the dash and toe-board during the crash can move the steering wheel from

its original design location and in some cases restrict the energy-absorbing feature of the

steering column and also affect the interaction of the airbag with the occupant. Therefore,

under conditions in which the toe-board and dash intrusions are substantially greater than

those in the baseline simulations, the predicted injury risks will be less than what it would

be if the intrusion effects of the steering column were modeled. This is the case of the

center pole impacts for which the current EFP does not predict the relatively elevated

serious injury rates observed in the field data when compared to the full engagement and

offset frontal impact configurations.

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• In certain impacts, further examination demonstrated that the injury risks predicted from

the femur loads dominated the combined injury risk. However, some simulations

involving high toe-board intrusions showed low femur loads because the knee was driven

up and knee-to-bolster interaction did not result in transmission of axial forces through

the femur. It became evident that an injury risk based only on axial femur loads, such as

the current KTH injury risk, is sensitive to geometry and is not as effective in predicting

the safety response of a vehicle class. An example of an impact with upward rotation of

the knee that resulted in lower recorded femur axial loads in spite of higher intrusions is

shown in Figure 4-12. This situation occurred in the vehicle-to-vehicle full engagement

of the Taurus Baseline into Taurus_ST at 35 mph. The corresponding femur loads and

dynamic intrusions are shown in Table 4-17.

• As noted earlier, generic restraint systems have been utilized in this study, and no

attempts at optimizing the restraints for individual vehicles were made when the occupant

models were developed. As such, the current implementation of EFP is more appropriate

for identifying changes in occupant risks from the baseline vehicle as mass, stiffness or

vehicle architecture are changed. The predicted trends are believed to be valid although

the absolute risks for a given baseline vehicle design may not be accurate.

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Figure 4-12. Example of Upward Knee Rotation Driven by High Intrusion in Taurus Baseline Occupant (right) as compared with Taurus_ST Occupant (left)

Table 4-17. Toe Pan Intrusions and Femur Loads for Example Impacts with Upward Knee Rotation

Max Toe Pan Intrusion Impact Speed (mph) Max Femur Load (N) (mm) Full Engagement Taurus Taurus Taurus_ST Taurus_ST Two-vehicle Crash Baseline Baseline 30 160 38 4447 6562 35 264 51 4893 8598

4.4.4. EFP Insight from Proof-of-Concept Application

The following observations were made from the proof-of-concept application of EFP to frontal crashes:

1. EFP allows safety evaluation of a vehicle at different crash configurations and speeds

representing the real world.

2. Utilizing the Taurus vehicle models, EFP was able to isolate the effect of mass and

stiffness changes in vehicle designs on total societal injury risk. This could not have been

achieved by physical experimentation.

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3. EFP allows evaluation of self-protection in single- and two-vehicle crashes. EFP also

allows concurrent evaluation of partner protection in two-vehicle crashes at multiple

configurations and speeds.

4. The EFP approach provides safety insights by body region, impact speeds, crash partner,

occupant size, and crash configuration.

4.5. Frontal EFP Application to Concept Lightweight Vehicle Designs

In this section, frontal EFP is applied to compute and assess the change in driver societal injury risk between a baseline midsize passenger car and a corresponding lightweight concept design, and between a baseline midsize Crossover Utility Vehicle (CUV) and two corresponding lightweight concept designs. The three lightweight concept vehicle designs were developed in recent projects to support Corporate Average Fuel Economy (CAFE) research efforts for

NHTSA. While these projects were conducted by different research groups and had slightly

different research goals and requirements, they all required the development of detailed finite

element (FE) models to demonstrate compliance with major federal safety standards and

consumer information safety requirements.

The midsize PC lightweight concept was designed by Electricore Inc. under contract with

NHTSA and was based off a 2011 Honda Accord achieving 23% mass reduction (Singh, et al.

2012). The specifications for the lightweight concept PC were to achieve the maximum amount of mass reduction appropriate for high volume production (200,000 vehicles per year) while maintaining the same vehicle functionalities, such as performance, safety, and crash rating with no more than a 10% cost increment compared to the baseline. The first midsize CUV lightweight concept was designed by FEV under contract to EPA and was based off a 2010 Toyota Venza with a target mass reduction of 20% (FEV 2012). The second CUV lightweight concept was

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Venza with a target mass reduction greater than 30%. The goal of both the CUV lightweight design studies was to identify mass saving opportunities while maintaining performance parity relative to the current vehicle (Lotus Engineering Inc. 2012).

The results of this application of EFP are presented and analyzed in the following sections, along with the proof-of-concept results to facilitate discussion and comparisons with the field data. Although the lightweight concept FE models were designed to match the features and capabilities, including exterior and interior dimensions, of specific vehicles by Honda and

Toyota, the lightweight designs were not made by the automotive manufacturers and thus cannot be affiliated with the manufacturers and do not reflect their design strategies. In this research, the societal risk comparisons are made with the baselines, however, the concept lightweight designs are named PC_LW, CUV_LW1, and CUV_LW2.

4.6. Vehicle & Occupant Models EFP Application Notes

The baseline vehicles for this application: Taurus, Accord, and Venza, have different levels of modeling accuracy and scopes of validation efforts (M. Marzougui, et al. 2012) (Lotus

Engineering Inc. 2012). Each of the concept design variants also has a different level of design and modeling accuracy. As such, the injury risk of each design concept will be evaluated relative to the baseline vehicle and not relative to each other. Also, the restraint systems used in all the vehicles are generic and were not optimized for the individual vehicles, so a direct comparison between the baseline vehicle risks might not be valid; however the trends from the baseline to the light-weighted designs should be valid. The same restraint system was used in the baseline and

the corresponding design concept occupant models. Using the same restraint system in the

baseline and design variants was intentional. This approach will provide insight on how the

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restraint systems need to be changed for future applications of EFP when improved restraint technologies become available to be incorporated in the models.

For this application of frontal EFP, the FE model weights are reported as provided by the various models developers, shown in Table 4-18. In this research, a check was made to ensure consistent reporting between a given baseline and corresponding lightweight concept model.

Table 4-18. Reported FEM weights by Model Developer: Baseline and Design Variants

Taurus Accord Venza Target Vehicle Taurus_LW Taurus_ST PC_LW CUV_LW1 CUV_LW2 Baseline Baseline Baseline

Weight (lbs) 3339 2508 3339 3681 2964 3980 3313 2537

For the Taurus targets, the reported FE model weight is based on the vehicle physical curb weight. For the Accord8 and Venza baselines and design variants, the reported FE model weights

are based on test weights in the frontal NCAP crash test, which includes dummies and

instrumentation. The physical vehicle weights are shown in Table 4-19.

Table 4-19. Baseline FE model weights

Curb NHTSA Crash Vehicle Model Year Weight NCAP- Test (lbs) type weight Venza BL MY 2009 3760 # 8603 3936 Accord BL MY 2009 3298 # 7098 3648

Taurus BL MY 2001-2004 3306 # 4776 3915

In the future, it is desirable that a consistent reporting of vehicle FE model weight is used for

frontal EFP applications involving mass studies. It is recommended that the actual simulated mass

8 The Accord baseline reported FE model weight was 3687lbs, per Electricore Report “2011 BaselineHonda Accord DTNH22-13-C-00320 Final Report 03.24.14”. 135

used in the full engagement crash simulation is used/adopted, since these models were developed

to best match available frontal NCAP-type tests from NHTSA. The reported EFP vehicle weights

should include the added mass due to time step control from LS-DYNA.

4.7. Frontal EFP Application to Concept Lightweight Vehicle Designs: Results and

Discussion

The EFP injury risk computations, as illustrated in the proof-of-concept application, were

performed for the following five target vehicle models: Accord baseline (BL), PC_LW, Venza

BL, CUV_LW1, and CUV_LW2. The injury risk computations were driven by the outputs from the EFP single- and two-vehicle crash simulations9, shown in the matrices in Table 3-24 and

Table 3-25 and repeated in Table 4-20 and Table 4-21 below for easy reference.

Table 4-20. Single-Vehicle Crash Simulations

Note: 15 LS-DYNA Simulations and 30 MADYMO (50%tile male & 5%tile female drivers) Simulations per Target Vehicle

9 The Venza baseline, CUV_LW1, and CUV_LW2 vehicle structure and occupant simulations were performed at NHTSA. The Accord baseline structural simulation was performed at EDAG, Inc. The PC_LW structural simulations and the Accord Baseline and PC_LW occupant simulation were performed by NCAC support staff. 136

Table 4-21. Two-Vehicle Frontal Crash Simulations

Note: 40 LS-DYNA Simulations and 160 MADYMO (50%tile male & 5%tile female drivers) Simulations for Target & Partner

The overall societal injury risks (SIR) predicted for the five target vehicles in this EFP implementation to frontal crashes are presented in Table 4-22. The results for the lightweight concept designs and their baselines are presented along with the proof-of-concept results to facilitate the discussion and comparisons. Given that the partner vehicles in this EFP implementation are surrogates of the current on-road modern vehicle fleet on U.S. roads, the computed societal risk will be referred to as the risk in a transitional fleet. The transitional fleet segments and corresponding surrogate vehicles are: Light PC ≤ 3106 lbs. (Yaris), Heavy PC >

3106 lbs. (Taurus), light LT ≤4594 lbs. (Explorer), and heavy LT >4594 lbs. (Silverado). In the case study presented in section 4.10, the fleet consisting of lightweight partner vehicles is referred to as a future fleet.

Table 4-22. EFP Computed Societal Risk for Target and Lightweight Concept Vehicle Designs

EFP Implementation EFP Proof-of-Concept EFP Application to Lightweight Designs Taurus Accord Venza Target Vehicle Taurus_LW Taurus_ST PC_LW CUV_LW1 CUV_LW2 Baseline Baseline Baseline Weight (lbs) 3339 2508 3339 3681 2964 3980 3313 2537 mass reduction 831 716 668 1444 % mass reduction 25% 0% 19% 17% 36% Societal Risk 2.27% 2.84% 2.80% 2.72% 3.34% 2.60% 2.80% 3.28% % Risk Increase 25% 23% 23% 7% 26% 137

Overall, there is a net societal injury risk increase for all the lightweight vehicle concepts as compared with their baseline designs (including the simplistic same weight but stiffer Taurus_ST

with high strength steel replacing all steel materials). Some of the increase in risk could be

explained by the decrease in mass; however additional analyses (refer to section 4.10) indicate

that differences in the vehicle structures of the lightweight concepts may also have contributed to

the increase in risk. Inasmuch as the lightweight concepts have both reduced mass and different

architectural designs, the increase in risks cannot be attributed to any one action. This is not the

case for the simplistic lightweight design of the Taurus where there was strictly a weight

decrease, since the density of the steel parts was simply lowered to achieve the 25% lighter

design concept. It is anticipated that optimized restraint systems will be implemented for both the

baselines and their lightweight designs to meet the existing regulatory and consumer information

test requirements. However, in the absence of additional performance requirements, relatively

increased societal injury risks for the lighter vehicles are still anticipated in real-world

interactions with the fleet.

Although the biomechanics injury risk functions implemented in frontal EFP were developed

for younger drivers, the predicted societal risks (i.e., cumulative serious injury risk) per target

vehicle are elevated when compared to the serious injury rates for the field population being

simulated (shown in Table 4-23 and Table 4-24).

Table 4-23. Field Serious Injury Rates for Younger Drivers

All Crashes , MY 2000+, airbag equipped, BES ≤40mph, belted, 16 ≤age≤50 All % all % of all rate of Frontal Taxonomy MAIS3+ Crashes Crashes MAIS3+F MAIS3+F Between Rail 100461 2672 10% 20% 2.7% FullEng 458254 4498 45% 34% 1.0% Offset 349204 4391 34% 34% 1.3% Other(Front) 61198 51 6% 0% 0.1% Sml Offset F&S 56079 1445 5% 11% 2.6% Total 1025195 13057 100% 100% 1.3%

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Table 4-24. Field Serious Injury Rates for Older Drivers

All Crashes , MY 2000+, airbag equipped, BES ≤40mph, belted, age> 50 All % all % of all rate of Frontal Taxonomy MAIS3+ Crashes Crashes MAIS3+F MAIS3+F Between Rail 31414 1195 10% 15% 3.8% FullEng 125908 3656 40% 45% 2.9% Offset 113010 2577 36% 32% 2.3% Other(Front) 21727 140 7% 2% 0.6% Sml Offset F&S 24120 471 8% 6% 2.0% Total 316178 8040 100% 100% 2.5%

To highlight the type of insights the EFP methodology and implemented SIR computation approach are capable of providing, the analyses in the following sections focus on two-vehicle crashes. The results and discussions provide some insight; in particular, the need to investigate improved injury risk functions in future studies, the need for detailed investigation of the real- world accident (or field) data, and the need for improved restraints to better address injuries occurring in the field. These insights demonstrate that the EFP methodology, in addition to providing an operative method for computation of societal injury risk for a target vehicle, contributes to the body of knowledge for vehicle crash safety by highlighting the areas that need improvement and further research.

4.7.1. SIR over Speed by Target Vehicle and Occupant Size

There is a societal injury risk increase across the all speed ranges simulated for the concept lightweight vehicle concepts as compared with their baseline designs in two-vehicle crashes, with exception of PC_LW at the higher speed ranges, as shown in Table 4-25. It is worth noting that although BES is a more consistent measure across crash configurations, it is expected to give lower ranges than delta-v for offset and between rail crashes given that it is a measure based on energy absorption.

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Table 4-25.Distribution of EFP Computed SIR for Target and Concept Vehicle Designs over Speed

Societal Risk in VTV over BES Combined AIS3+ risk of Head, Neck, Chest & Femur 17

The SIR is presented as a relative percentage of total risk (100%) in each target in Figure

4-13, along with the average risk for passenger cars ≥ 3106 lbs. and <3106 lbs. from the field. To allow a sufficient sample size, the field population included NASS CDS national estimates of serious injuries in both single- and two-vehicle frontal crashes (full engagement + offset +

between rail configurations), with BES ≤ 40 mph and younger drivers in airbag-equipped vehicles of model year 2000 or later.

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Figure 4-13. Distribution of Societal Injury Risk over BES in VTV by Target Vehicle

The majority of the predicted societal risk is at the lower speed ranges for all the vehicle targets analyzed (baseline and lightweight vehicle concepts). This trend is similar to the field data. The predicted societal risk provide a good match with the field data in the middle speed ranges; however, there is an over prediction of the contribution of societal risk at the lowest speed ranges, while an under prediction at the highest speed ranges. The following interpretations all affect the relative prediction at the lower speeds:

• There is under-prediction of lower extremity injuries at the higher speeds. As discussed in

section 3.13.5, it is expected that the serious injury risk function for the lower extremities,

i.e., Knee-Thigh-Hip (KTH) complex, would underestimate real-world serious injury

rates, given the established finding by earlier research that the risk function for moderate

lower extremities substantially under-predicts injury rates for the Knee-Thigh-Hip (KTH)

complex in the field.

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• Better fidelity injury risk functions, in particular for the chest, are needed at the lower

speed ranges. To date, injury risk functions have been developed by the biomechanics

research community using the available data at higher speed ranges, i.e., near the

regulatory impact requirements. Therefore, the injury risk functions need careful

examination and, possibly, further development for improved prediction at the lower

speeds. This is exacerbated by the reduced sensitivity of Hybrid III dummies at the lower

speeds. This applies to the physical test devices and thus the corresponding virtual

models used in simulations, given that those have been developed to match the physical

dummy responses.

• In addition, the restraint systems in the models are modern, i.e., vintage 2006-2011 MY

designs, and are fine-tuned to perform well in single-vehicle frontal regulations and

consumer information test protocols at the higher speeds.

The contribution of the 5th percentile female driver to the societal injury risks for the eight target vehicles is presented in Figure 4-14.

Figure 4-14. Contribution of 5th Percentile Female Driver to SIR in VTV by Target Vehicle 142

Although 25% of drivers are modeled by the small size 5th percentile female dummy, they account for 30-50% of the serious injuries at the lower speed ranges; in particular, the small statured driver is overrepresented in the societal risk for the Venza baseline and lightweight

design concepts.

4.7.2. Contribution to SIR by Body Region over BES

In addition to the overall societal injury risk, the risk for each body region was aggregated

over impact speed, crash configuration, occupant size, and crash events. The individual body

region SIRs are presented in Table 4-26. With the exception of an increased head injury SIR for

the PC_LW when compared to the Accord baseline, there is an increase in all the body region

societal injury risks for the concept lightweight vehicles as compared with their baseline designs in two-vehicle crashes.

Table 4-26. Contribution of Body Region: Head, Chest, Femur, and Neck to overall SIR

Taurus Accord Venza Target Vehicle Taurus_LW Taurus_ST PC_LW CUV_LW1 CUV_LW2 Baseline Baseline Baseline

Weight (lbs) 3339 2508 3339 3681 2964 3980 3313 2537 mass reduction 831 716 668 1444 % mass reduction 25% 0% 19% 17% 36% Societal Risk 2.27% 2.84% 2.80% 2.72% 3.34% 2.60% 2.80% 3.28%

% Risk Increase 25% 23% 23% 7% 26% SocietalSocietal Risk Risk- I - head Target + Partner Combined AIS3+ risk of Head, Neck, Chest & Femur 0.027% 0.031% 0.067% 0.073% 0.057% 0.059% 0.081% 0.208% Societal onlyRisk II - Target + Partner Combined AIS3+ risk of Head, Neck, and Chest Societal Risk IIP - Target + Partner Combined AIS3+ risk of Head, Neck, and Chest with A-Pillar Intrusion Penalty Head Risk Increase 16% 148% -22% 36% 250% Societal Risk - 1.71% 2.07% 2.03% 2.35% 2.85% 1.97% 2.04% 2.31% chest only Chest Risk Increase 21% 18% 21% 4% 18% Societal Risk - 0.49% 0.69% 0.67% 0.29% 0.41% 0.50% 0.55% 0.63% femur only Femur Risk Increase 40% 36% 43% 10% 26% Societal Risk - neck 0.06% 0.08% 0.07% 0.03% 0.04% 0.10% 0.16% 0.18% only Neck Risk Increase 31% 11% 45% 54% 73%

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While overall, the SIR based on a combined injury risk index is a relatively good match with the field, when considering societal risk by body region, although the trends are meaningful, the

EFP results point to the following:

For the chest: Although a higher contribution for chest injuries is expected, the predicted

level is high. A better fidelity injury risk function at the lower chest deflections, i.e., in the range

of 20-35 mm, would be useful (as a demonstration, a case study of an implementation of EFP

with an alternate risk function is presented in the next section). In addition, it is worthwhile to

investigate how real people wear seat belts across their chests compared to the NCAP and IIHS

test protocols, given the sensitivity of chest deflection response to belt placement. This affects the

validation of the occupant models to available crash test data. A multiple point chest

measurement in the dummy is an area of research and such a measurement would provide a

higher fidelity chest deflection measurement in the physical test device and correspondingly for

simulations.

For the head: The restraints implemented in EFP, including a modern airbag, along with the

modeled crash configurations, result in consistent interactions of the head with the airbag. This

results in low values of HIC (which is not related to the fidelity of the head injury risk function at lower severity levels). Serious head injuries in the field typically result from skull fractures when the head impacts a hard object. An investigation of the injuring contacts, as coded by NASS investigators in frontal crashes with serious head injuries, was performed and results are presented in Table 4-27. An in-depth study of the pertinent NASS cases would provide more

insight on the injury mechanism; however, the steering wheel or airbag was coded as the injury

source in over 32% of the cases, and an object to the left of the driver, interior or exterior to the

vehicle, was coded as the injuring source in 29% of the cases. Such data highlights the need to study airbag field performance, including the effectiveness of the airbag deployment thresholds or 144

other characteristics, as well as the need to consider oblique crash configurations for simulation of frontal crashes in future implementations of EFP.

Table 4-27. Injury Sources for BAIS3+ Head Injuries in Frontal Crashes MY 85+ airbag equipped vehicles, nearside belted driver AGE > 16, NASS CDS National Estimates

Other Windshield/ Left interior Instrument Vehicle/obj, Steering Visor/Front Left /door panel panel/Knee Belt Non Roof Left wheel Header A-Pillar /hardware Airbag bolster buckle/web Contact Rail/ B-Pillar Other (n=42) (n=12) (n=10) (n=9) (n=12) (n=11) (n=6) (n=5) (n=13) Roof (n=2) (n=13) All Frontal 2113 1255 1015 764 1151 629.45 390 479 1163 297 901 Crashes % of all 21% 12% 10% 8% 11% 6% 4% 5% 11% 3% 9%

For the femur: As discussed earlier, the current injury risk function is based on femur axial force alone and may not be as effective in addressing the real-world injury mechanism for lower extremities. It is expected that this results in under-prediction of injuries to this body region at the higher crash speeds. This is an area of current biomechanics research and EFP just sheds more light on this issue. In the simulations, a femur index based on the femur moment about the y-axis has been computed for future analyses, which could involve replacing the axial force base injury risk with one based on the index for better prediction of lower extremity injuries.

4.7.3. Contribution to SIR by Body Region over BES

The contribution of head, chest, and femur serious injuries over BES to the combined injury risk is shown in Figure 4-15, Figure 4-16, and Figure 4-17. In general, the chest is the highest contributor to societal risk for all the target vehicles across speed ranges for all the targets. There is an increase in the contribution of head injuries to societal risk in the higher speeds, specifically for the Accord baseline and CUV_LW2. There is also a gradual increase in the contribution of femur injuries in the higher speed ranges, specifically for the Taurus and corresponding simple design concepts.

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Figure 4-15. Contribution of Head Serious Injury to SIR over BES by Target Vehicle

Figure 4-16. Contribution of Chest Serious Injury to SIR over BES by Target Vehicle

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Figure 4-17. Contribution of Femur Serious Injury to SIR over BES by Target Vehicle

The distributions of head societal injury risk over BES and the corresponding contributions by the 5th percentile female driver to the head SIR overall risk are presented in Figure 4-18 and

Figure 4-19. Keeping in mind that the head societal risk values are small, the head societal risk is lower in the middle speed ranges than at the lowest and highest speed for the Taurus and design

concepts. The risk for the Accord BL increases at the higher speed range, while this is not

observed for the PC_LW. The risk for the CUV_LW2 increases at the highest speed range while

this is not observed for the baseline and CUV_LW1. Overall, in the field data, 50% of the serious

injuries occur in crashes with BES ≤ 22 mph, which compares well with the predicted

distribution. As shown in Figure 4-19, the small statured driver is main contributor to the head

societal risk at the lower speed ranges.

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Figure 4-18. Head Injury SIR over BES by Target Vehicle

Figure 4-19. Contribution of 5th Percentile Female Driver to Head SIR over BES by Target Vehicle

The distributions of chest societal injury risk over BES and the corresponding contributions by the 5th percentile female driver to the chest SIR overall risk are presented in Figure 4-20 and

Figure 4-21.

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Figure 4-20. Chest Injury SIR over BES by Target Vehicle

Figure 4-21. Contribution of 5th Percentile Female Driver to Chest SIR over BES by Target Vehicle

The chest societal injury risk is elevated in the low speed ranges and decreases substantially at the higher speeds. This is consistent for all the target vehicles. The small statured driver’s contribution to the societal chest injury is generally consistent across speed ranges for each of the

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target vehicles and its corresponding lightweight concept designs. The contribution of the small

statured driver to the societal risk is elevated at close to 50% for the Venza and its lightweight

designs at speed ranges up to 27 mph. The small statured driver is overrepresented in the chest

societal injury risk for the Taurus and Venza, and their corresponding lightweight concept

designs.

The distributions of femur societal injury risk over BES and the corresponding contributions

by the 5th percentile female driver to the femur SIR overall risk are presented in Figure 4-22 and

Figure 4-23. The femur societal risk is mainly in the low speed ranges and decreases with speed.

This is consistent for all the target vehicles.

Figure 4-22. Femur Injury SIR over BES by Target Vehicle

The small statured driver’s contribution to the femur societal injury risk is similar and decreases with speed for the Taurus and Taurus_LW from 42% to around 10%; however, for the

stiffer but same weight Taurus_ST concept design, the contribution is only 17% at the low speeds

and decreases to 13% at the high speed range. The contribution for the Accord baseline and

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PC_LW is similar and decreases from around 30% for both at the low speeds to 12% for the baseline and 17% for PC_LW at the high speed ranges. The contribution of the small statured driver to the femur societal injury risk is around 40% for the Venza baseline and lightweight concept designs, and generally decreases at higher speeds to 15% for the baseline and around

20% for both the CUV_LW1 and CUV_LW2.

Figure 4-23. Contribution of 5th Percentile Female Driver to Femur SIR over BES by Target Vehicle

The distributions of neck societal injury risk over BES and the corresponding contributions

by the 5th percentile female driver to the neck SIR overall risk are presented in Figure 4-24 and

Figure 4-25.

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Figure 4-24. Neck Injury SIR over BES by Target Vehicle

Figure 4-25. Contribution of 5th Percentile Female Driver to Neck SIR over BES by Target Vehicle

Keeping in mind that the neck societal risk values are small, the neck societal risk is greater at the lower speeds for all the target vehicles. The small statured driver is the main contributor to the neck societal risk across all speed ranges for all the target vehicles. With the exception of the

Venza and its lightweight design concept designs, the contribution gradually decreases at the 152

higher speeds. The Accord and PC_LW have similar contributions across most of the speed

ranges, with a decrease at the high speed range.

4.8. Alternate Chest Injury Risk Function: Case Study

In this section, an alternate chest injury risk function was implemented as a case study on the insights that EFP can give on the suitability of injury risk functions. This resulted in a different but more realistic contribution of chest SIR to the overall societal risk, by crash configuration and body regions over the simulated BES ranges.

4.8.1. Selection of Alternate Chest Injury Risk Function

In the application to the lightweight concept designs and corresponding baselines presented in the previous section, EFP predicted an elevated contribution of chest injuries to the overall SIR, specifically at the lower speed ranges. This highlighted the need for an improved chest injury risk function for assessment and prediction of chest injury risk at the lower chest deflections, i.e., in the 20-35 mm range, which is a typical occupant response of vehicles performing well in current

(up to 2011, at least) regulatory and consumer information test protocols. As noted earlier, injury risk functions to date have been developed using the available data at higher speed ranges, i.e., near the regulatory testing requirements. Therefore, the injury risk functions need careful examination and, in the case of the chest, further development, for improved prediction at the lower speeds.

An alternate injury chest risk function for the Hybrid III 50th percentile male driver was

obtained from the literature and is presented as Equation 4-2 (Prasad, Laituri and Sullivan,

Estimation of AIS>=3 thoracic injury risks of belted drivers in NASS frontal crashes 2004). This

risk function captured trends in aggregate NASS field data and was validated for point estimates in numerous crashes. 153

th (9.607-0.179*(defl)) For 50 %tile male dummy: PAIS3+(defl) = 1/(1+ e ) (4-2)

Since the small female chest stiffness is softer than that of the midsize male, a scaling factor of 1.2, typically applied in the biomechanics literature, was incorporated in the equation from

Prasad et al. (Mertz and Irwin October 2003). This provided an alternate chest injury risk function for the Hybrid III 5th percentile female driver, presented as Equation 4-3.

th (9.607-0.179*(1.2*defl)) For 5 %tile female dummy: PAIS3+(defl) = 1/(1+ e ) (4-3)

The original and alternate chest injury functions are plotted in Figure 4-26. The alternate risk functions for both dummy sizes estimated reduced injury risks in the 20-30 mm range of interest.

Figure 4-26. EFP Chest Injury Risk Functions for Hybrid III Midsize Male and Small Female Dummies

4.8.2. Comparison of Chest and Overall Societal Injury Risk

For the implementation of the 2004 Prasad alternate chest injury risk function, EFP was

applied for five targets, the Taurus baseline and two simple design variants, and the Accord

baseline and its lightweight concept design. The new chest SIR, i.e., aggregated over speed, crash

configuration, crash partner, and crash event, is shown on the left side of Table 4-28. On the right 154

side of the table, the EFP chest SIR with the original, i.e., NCAP, chest injury risk function that was presented in the previous section is shown. There was a substantial reduction in chest societal injury risk, ranging from a 3.7-4.3 times decrease, with the alternate injury risk function; however, the risk increase between the baselines and corresponding lightweight designs was comparable.

Table 4-28. Chest SIR by Target Vehicle: NCAP versus 2004 Prasad Risk Function

Taurus Accord Taurus Accord Target Vehicle Taurus_LW Taurus_ST PC_LW Target Vehicle Taurus_LW Taurus_ST PC_LW Baseline Baseline Baseline Baseline

Weight (lbs) 3339 2508 3339 3681 2964 Weight (lbs) 3339 2508 3339 3681 2964 reduction 831 716 reduction 831 716 % mass reduction 25% 0% 19% % mass reduction 25% 0% 19% Societal Risk - Societal Risk - Chest only (new 0.40% 0.48% 0.50% 0.61% 0.77% 1.71% 2.07% 2.03% 2.35% 2.85% chest only risk function) Risk Increase 21% 27% 26% Chest Risk Increase 21% 18% 21%

The overall societal risk was reduced in the EFP implementation as shown in the comparison

between the SIR computed with the alternate chest injury risk function (on the left) and the

original NCAP chest injury risk function (on the right) in Table 4-29. With the alternate chest

injury risk function, there is a similar but somewhat larger societal risk increase for the concept

lightweight vehicle concepts as compared with their baseline designs.

Table 4-29. Overall SIR by Target Vehicle: NCAP versus 2004 Prasad Risk Function

Taurus Accord Taurus Accord Target Vehicle Taurus_LW Taurus_ST PC_LW Target Vehicle Taurus_LW Taurus_ST PC_LW Baseline Baseline Baseline Baseline Weight (lbs) 3339 2508 3339 3681 2964 Weight (lbs) 3339 2508 3339 3681 2964 reduction 831 716 mass reduction 831 716 % mass reduction 25% 0% 19% % mass reduction 25% 0% 19% Societal Risk (new chest 0.97% 1.27% 1.30% 0.99% 1.27% Societal Risk 2.27% 2.84% 2.80% 2.72% 3.34% injury risk) Risk Increase 31% 34% 28% % Risk Increase 25% 23% 23%

The overall societal risk seems to provide a better match to the field injury rates of the

younger driver presented in Table 4-23, given the following considerations:

• The restraint systems used in the fleet models are modern (2006-2011 timeframe).

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• Upper extremities are not addressed in this analysis or by the test protocols or the

biomechanics research community to date, although they account for 20% of serious

injury in the field.

The above implies that the overall societal risk predicted by EFP is expected to be lower than

the NASS field aggregated risk, shown again in Table 4-30 below for ease of reference.

Table 4-30. Field Serious Injury Rates for Younger Drivers

4.8.3. Comparison of SIR Trends for Taurus & Accord Targets

As in Figure 4-13, the SIR computed with the alternate chest risk function is presented over

BES in Figure 4-27 as a relative percentage of total risk (100%) in each target in along with the average risk for passenger cars ≥ 3106 lbs. and <3106 lbs. from the field. Although the SIR has similar trends over BES, the EFP implementation with the alternate injury risk function is an improved match to the field data.

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Figure 4-27. Distribution of SIR over BES in VTV by Target Vehicle- Alternate Chest Injury Risk

The contributions of head, chest, and femur serious injuries to the overall SIR are presented side-by-side for the EFP implementation with the alternate chest injury function and the original implementation in Figure 4-28 through Figure 4-31.

Figure 4-28. Contribution of Head Injury to SIR- 2004 Prasad Chest Injury Risk Function (left) versus NCAP (right)

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Figure 4-29. Contribution of Chest Injury to SIR- 2004 Prasad Chest Injury Risk Function (left) versus NCAP (right)

Figure 4-30. Contribution of Femur Injury to SIR- 2004 Prasad Chest Injury Risk Function (left) versus NCAP (right)

With the alternate chest injury risk function, there is an increase in the contribution of head injuries to the societal injury risk, more so at the higher speed ranges. However, the chest contributes considerably less to the societal risk across speed ranges for both the Taurus and

Accord targets and corresponding lightweight concepts, with a more pronounced reduction for the

Taurus vehicles. Correspondingly, there is a substantial increase in the contribution of femur injuries to societal risk across the speed ranges, for both the Taurus and Accord vehicles and their lightweight design concepts, with a more pronounced increase at the lower speed ranges. 158

Figure 4-31. Contribution of 5th Percentile Female Driver to SIR- 2004 Prasad Chest Injury Risk Function (left) versus NCAP (right)

With the alternate chest injury risk function, the contribution of the small statured driver to the societal risk across speed ranges for the Taurus has changed. At the lowest speed the contribution increased while at the higher speed ranges, the contributions have decreased substantially. There is a similar but less pronounced change for the Accord BL and its lightweight concept design.

4.9. EFP Scenario Analysis: Fleet Composition Changes

To illustrate the capability of EFP to assess the effect of fleet composition changes on societal injury risk, the following “what-if” scenarios were performed using the available vehicle and occupant models:

1. What if all SUVs were replaced by CUVs in the fleet?

2. What if the lighter weight car was removed from the fleet and all cars were represented

by the heavier car in the fleet?

3. What if the heavier weight car was removed from the fleet and all cars were represented

by the lighter car in the fleet?

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In the following sections, the predicted EFP societal risks are presented for each of the three scenarios. While the fleet is changing in the “what-if” scenarios, the same target vehicles are used as applicable, i.e., Taurus baseline and its simple design variants, and the Accord and Venza baselines and their corresponding lightweight concept designs.

4.9.1. 1st Scenario: All SUVs are CUVs

In this scenario, the fleet partner vehicles represent a light PC segment (with Yaris as

surrogate), a heavy PC segment (with Taurus as surrogate), a CUV segment (with Venza as

surrogate), and a pickup segment (with Silverado as surrogate). The EFP-predicted SIR for the

baseline fleet and the 1st scenario fleet are presented in Table 4-31 and Table 4-32. This scenario required two-vehicle full engagement and offset impact structural and occupant simulations with the Venza as the partner for the eight target vehicles; however the EFP weighting factors were the same as in the baseline fleet.

Table 4-31. Baseline Fleet: Yaris, Taurus, Explorer, and Silverado

Taurus Accord Venza Target Vehicle Taurus_LW Taurus_ST PC_LW CUV_LW1 CUV_LW2 Baseline Baseline Baseline

Weight (lbs) 3339 2508 3339 3681 2964 3980 3313 2537 mass reduction 831 716 668 1444 % mass reduction 25% 0% 19% 17% 36% Societal Risk 2.27% 2.84% 2.80% 2.72% 3.34% 2.60% 2.80% 3.28%

% Risk Increase 25% 23% 23% 7% 26%

Table 4-32. 1st Scenario Fleet: Yaris, Taurus, new Venza and Silverado

Taurus Accord Venza Target Vehicle Taurus_LW Taurus_ST PC_LW CUV_LW1 CUV_LW2 Baseline Baseline Baseline

Weight (lbs) 3339 2508 3339 3681 2964 3980 3313 2537 reduction 831 716 668 1444 % mass reduction 25% 0% 19% 17% 36% Societal Risk 2.26% 2.86% 2.79% 2.68% 3.12% 2.61% 2.69% 3.00%

Combined Risk Increase 27% 24% 17% 3% 15% 160

In this scenario, there remained a net societal risk increase for all the concept lightweight vehicle concepts as compared with their baseline designs in the fleet; however there was a smaller increase in risk for PC_LW from 23% to 17%, for CUV_LW1 from 7% to 3%, and for

CUV_LW2 from 26% to 15%.

4.9.2. 2nd Scenario: All PCs are Heavy PCs

In this scenario, the fleet partner vehicles represent a heavy PC segment (with Taurus as

surrogate), an SUV segment (with Explorer as surrogate), and a pickup segment (with Silverado

as surrogate). The EFP-predicted SIRs for the 2nd scenario fleet are presented in Table 4-33. The societal injury risks for the applicable targets, i.e., all PC targets greater than 3106 lbs., would be compared with the SIRs for the same targets interacting with the baseline fleet, shown in Table

4-32. This scenario did not require any additional single- or two-vehicle simulations, however,

the vehicle class weighting factors were modified to implement the scenario of a homogeneous

passenger car segment in which the impacts with the heavy PC where given the weight of the

whole passenger car class and the impacts with the light PC were ideally giving a weight of zero.

Table 4-33. 2nd Scenario Fleet: Taurus, Explorer, and Silverado

Taurus Accord Venza Target Vehicle Taurus_ST CUV_LW1 Baseline Baseline Baseline

Weight (lbs) 3339 3339 3681 3980 3313 reduction 668 % mass reduction 0% 17% Societal Risk 2.13% 2.51% 2.57% 2.46% 2.71%

The societal risk decreased for all the target vehicles as follows, as compared with the

baseline fleet:

1. Taurus Baseline and Taurus_ST from 2.27% to 2.13%, from 2.80% to 2.51%, i.e., by 6%

and 10% 161

2. Accord Baseline from 2.72% to 2.57%, i.e., by 5.5%.

3. Venza Baseline and CUV_LW1 from 2.6% to 2.4%, from 2.8% to 2.71%, i.e., by 8% and

4.9.3. 3rd Scenario: All PCs are Light PCs

In this scenario, the fleet partner vehicles represent a light PC segment (with Yaris as surrogate), an SUV segment (with Explorer as surrogate), and a pickup segment (with Silverado as surrogate). The EFP-predicted SIRs for the 3rd scenario fleet are presented in Table 4-34. The societal injury risks for the applicable targets, i.e., all PC targets less than 3106 lbs., would be compared with the SIRs for the same targets interacting with the baseline fleet, shown in Table

4-32. This scenario did not require any additional single- or two-vehicle simulations, however,

the vehicle class weighting factors were modified to implement the scenario of a homogeneous

passenger car segment in which the impacts with the light PC where given the weight of the

whole passenger car class and the impacts with the heavy PC were ideally giving a weight of

zero.

Table 4-34. 3rd Scenario Fleet: Yaris, Explorer, and Silverado

The societal risk increased as follows, for all target vehicles except the CUV_LW2 as compared with the baseline fleet:

1. Taurus_LW from 2.84% to 2.92%, i.e., by about 3%.

2. PC_LW target from 3.34% to 3.48%, i.e., by 4%.

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3. Decreased for the CUV_LW2 from 3.28% to 3.15%, i.e., 4%.

4.10. Frontal EFP Application: Lightweight vs. Baseline Fleet Safety Analyses

The analyses discussed in the previous sections provide safety insight and assessment of the change in societal risk for a target vehicle of interest in a transitional fleet. As noted earlier, the partner vehicles in the initial EFP implementation are surrogates of the current modern vehicle fleet on U.S. roads and the computed societal injury risk is relevant for the near future in a

transitional fleet.

In this section, the objective is to examine safety impacts due to the interaction between light-

weighted vehicle designs to better understand the future U.S. fleet.

4.10.1. EFP Approach to Illustrate Baseline versus Lightweight Fleet Safety

Interactions

Given the availability of lightweight concept vehicle designs for two vehicle segments,

passenger car (PC) and Crossover Utility Vehicle (CUV), the approach is to apply EFP to obtain

insights into the safety interactions in an assumed light-weighted fleet as compared with a

baseline fleet, both consisting of two vehicle segments. The focus of this analysis is two-vehicle

impacts. A baseline fleet and two concept lightweight fleet options are defined as follows for this analysis:

• The baseline fleet is composed of two segments: PC (Accord Baseline) and CUV (Venza

Baseline). There are two baseline targets: Accord and Venza.

• The first lightweight feet (LW1 fleet) is composed of two segments: PC_LW (to be

referred to as Accord LW), CUV_LW1 (to be referred to as Venza Low Option (LO)).

There are two lightweight targets: Accord LW and Venza LO.

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• The second lightweight feet (LW2 fleet) is composed of two segments: PC (Accord

LW), CUV_LW2 (to be referred to as Venza High Option (HO)). There are two

lightweight targets: Accord LW and Venza HO.

Caveat: The change of nomenclature of Accord LW and Venza LO and HO is strictly to ease

the discussion and presentation of the results for this analysis. There is no implication that these

lightweight concept designs are affiliated with either automotive-manufacturer nor is there an inherent assumption that the concept vehicles reflect Honda’s or Toyota’s light-weighting design strategies

4.10.1.1. EFP Weighting for Vehicle Class Exposure

The EFP weighting factors, i.e., real-world frequency of occurrence, for vehicle class exposure for the baseline and two lightweight fleets are based on 2010 GES crash involvement by vehicle segments modeled in the fleets (NHTSA 2012). The distributions of crash pairs to be simulated are highlighted in Table 4-35 and the EFP weighting factors for vehicle class exposure for the two segments fleet are shown in Table 4-36.

Table 4-35. Distributions for Crash Pairs in Baseline, LW1, and LW2 Fleet Simulations

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Table 4-36. EFP Vehicle Class Weighting for Baseline, LW1, and LW2 Fleets

4.10.1.2. EFP Crash Configuration Exposures and Impact Speed Weighting

The EFP weighting factors developed in section 3.10 for two-vehicle crash configurations are

applied in this analysis (as shown in Table 3-27). The impact speed weighting factors for PC ≥

3,106 lbs. and LT <4594 lbs. targets developed in section 3.12 and shown in

Table 4-37 and Table 4-38 below are applied for the targets in the baseline, LW1, and LW2 fleet two-vehicle impact simulations.

Table 4-37. EFP Impact Speed Weighting for Accord Targets

NASS Distributions used for Accord Targets (PC ≥3,106 lbs) for the full engagement & offset frontal impact comfigurations

Speed (mph) PC Full PC offset CUV Full CUV Offset

0-12 50.1% 69.7% 50.1% 69.7% 12-17 31.1% 23.0% 31.1% 23.0% 17-22 13.1% 4.5% 13.1% 4.5% 22-27 4.0% 1.6% 4.0% 1.6% 27-32 1.2% 0.9% 1.2% 0.9% ≥32 0.5% 0.4% 0.5% 0.4% 100% 100% 100% 100%

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Table 4-38. EFP Impact Speed Weighting for the Venza Targets

NASS Distributions used for Venza targets (LT <4594lb lbs) for the full engagement & offset frontal impact comfigurations

Speed (mph) PC Full PC offset CUV Full CUV Offset

0-12 38.8% 53.3% 38.8% 53.3% 12-17 34.8% 32.2% 34.8% 32.2% 17-22 15.1% 8.1% 15.1% 8.1% 22-27 6.2% 4.9% 6.2% 4.9% 27-32 4.2% 0.8% 4.2% 0.8% ≥32 0.8% 0.7% 0.8% 0.7% 100% 100% 100% 100%

It is assumed that the Accord LW target undergoes similar BES distribution in the LW1 and

LW2 fleets as the Accord BL in the baseline fleet (since drivers are not expected to change their driving behavior for given vehicle segment when light-weighted). By similar reasoning, it is also assumed that all Venzas in the baseline and lightweight fleets undergo BES distribution of the lighter LT in the current fleet.

4.10.1.3. Vehicle FE Model Physical Weights

In this EFP application, the societal injury risk is being assessed on a fleet level, i.e., between a baseline fleet (albeit composed of only two segments for this initial implementation) and a future lightweight fleet of concept vehicles (again made up of two segments). As such, a consistent reporting of vehicle FEM weights is needed. The actual simulated mass used in the full engagement crash simulation is used. It is worth noting that these models were developed to best match available frontal NCAP-type tests from NHTSA. The reported EFP vehicle weights include the added mass due to time step control from LS-Dyna and are shown in Table 4-39 along with vehicle curb and crash test weights.

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Table 4-39. Accord and Venza Baseline Vehicle Weights

EFP FEM Curb NHTSA Crash Test Model Vehicle weights Weight NCAP-type weight Year (lbs) (lbs) Test # (lbs) Venza BL MY 2009 3980 3760 # 8603 3936

Accord BL MY 2009 3783 3298 # 7098 3648

4.10.2. Societal Risk in Lightweight Fleets vs. Baseline Fleet

The EFP risk computations were performed and the overall SIR for the baseline, LW1, and

LW2 fleets, and the corresponding PC and CUV segments are presented in Table 4-40.

Table 4-40. Fleet and Segment SIR: Lightweight versus Baseline Fleets

Overall, there is net safety decrease on a fleet level and by vehicle segments modeled in the

light-weighted fleets. Further analyses, as outlined in the section below, demonstrate that the

lightweight concept vehicles actually have different structural performance than their respective

baselines. In the lightweight fleets, the mass ratios have changed and have actually reversed

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between the two segments, i.e., passenger car vs. CUV. Some of the increase in risk can be

explained by the increase in mass ratios between the two segments (Evans 2004), however, the

difference in vehicle structural design seems to have a greater effect, especially in the lower speed

crashes.

4.11. Baseline and Lightweight Concept Vehicles Comparisons

The following additional analyses of intermediate EFP computations and relevant vehicle

responses were performed for the target vehicles to get a better understanding of the computed

societal injury risk:

• Detailed occupant responses & injury risk by body region over impact speed

• Compartment acceleration pulse comparison

• Vehicle Pulse Index (VPI10) assessment

• Maximum dynamic intrusions

• Total FE internal energies (at simulation end)

Note: It is not practical or feasible to measure dynamic intrusion and internal energies in

physical crash tests. This is one advantage of FEM simulation and fleet modeling.

4.11.1. Accord Targets Comparisons

The Accord driver responses in the two-vehicle full engagement frontal self-impact are presented in Table 4-41 through Table 4-44 for the midsize male and small female driver in both the baseline and lightweight concept.

10 ISO/TR 12353-3:2013, Road vehicles -- Traffic accident analysis -- Part 3: Guidelines for the interpretation of recorded crash pulse data to determine impact severity. 168

Table 4-41. Accord Baseline Full Engagement Impact- Midsize Male Driver Responses

HIII 50th %ile Dummy Chest Neck Chest Neck Combined Combined Crash Speed Femur HIC15 Femur HIC15 Deflectio Tension Deflectio Tension Injury Risk Injury Risk II Configuration (mph) Max (N) Risk (%) Max (%) n (mm) (N) n (%) (T)(%) (%) (No Femur) 15 55 27 401 896 0.0% 3.3% 0.1% 0.0% 3.4% 3.3% 20 51 27 398 812 0.0% 3.3% 0.1% 0.0% 3.4% 3.3% Accord BL Full 25 66 28 1267 931 0.0% 3.8% 0.3% 0.0% 4.2% 3.8% Engagement 30 72 29 1903 881 0.0% 4.2% 0.6% 0.0% 4.8% 4.2% 35 120 30 2887 862 0.0% 4.7% 1.0% 0.0% 5.7% 4.7%

Table 4-42. . Accord LW Full Engagement Impact- Midsize Male Driver Responses

Table 4-43. Accord Baseline Full Engagement Impact- Small Female Driver Responses

HIII 5th %ile Dummy

Chest Chest Neck Combined Combined Crash Speed Femur Max Neck HIC15 Risk Femur Max HIC15 Deflection Deflection Tension Injury Risk Injury Risk II (No Configuration (mph) (N) Tension (N) (%) (%) (mm) (%) (T)(%) (%) Femur) (%)

15 164 25 378 1108 0.1% 4.7% 0.1% 0.1% 5.0% 4.9% 20 170 24 659 992 0.1% 4.3% 0.3% 0.1% 4.7% 4.4% Accord BL Full 25 185 24 1291 1042 0.1% 4.5% 0.6% 0.1% 5.3% 4.7% Engagement 30 207 25 1282 1174 0.2% 5.0% 0.6% 0.1% 5.8% 5.3% 35 250 26 1617 1282 0.5% 5.4% 0.8% 0.2% 6.8% 6.0%

Table 4-44. Accord LW Full Engagement Impact- Small Female Driver Responses

HIII 5th %ile Dummy Chest Neck Chest Neck Combined Combined Crash Speed Femur HIC15 Risk Femur HIC15 Deflection Tension Deflection Tension Injury Risk Injury Risk II Configuration (mph) Max (N) (%) Max (%) (mm) (N) (%) (T)(%) (%) (No Femur) 15 173 25 573 1149 0.1% 4.8% 0.2% 0.1% 5.2% 5.0% 20 234 25 973 1194 0.3% 5.0% 0.4% 0.2% 5.9% 5.5% Accord LW Full 25 261 26 1187 1322 0.5% 5.5% 0.5% 0.3% 6.7% 6.2% Engagement 30 272 26 2403 1241 0.6% 5.7% 1.4% 0.2% 7.8% 6.5% 35 278 26 2208 1239 0.7% 5.7% 1.3% 0.2% 7.7% 6.5% 169

There was an increase in societal risk for both the 50th percentile male and 5th percentile female drivers in the Accord lightweight concept design as compared with the baseline. This trend was seen across all body regions. The bigger increases were at the lower speeds, e.g., there was a 12% increase in societal risk in the 35 mph full engagement impact but a 50% increase in the 20 mph impact for the 50th percentile male driver. Similarly, there was a 13% increase in

societal risk in the 35 mph impact but a 25% in the 20mph impact for the 5th percentile female

driver.

4.11.1.1. Accord Compartment Acceleration Pulse Comparison

The compartment acceleration pulse comparisons for the Accord targets are presented in

Figure 4-32. The shorter duration pulses for the Accord lightweight concept indicate a stiffer front-end design than that of the baseline.

Figure 4-32. Accord Baseline versus Lightweight Compartment Pulse Comparisons at 25 and 35 mph

The vehicle pulse severity, as demonstrated by VPI shown in Figure 4-33, is higher at the

lower speeds for the lightweight design concept; the difference at the lower speed is more than at

the higher speed. The VPI analysis indicates that the lightweight concept Accord finite element

model was developed to have a similar crash pulse severity as the baseline Accord at the 35 mph

NCAP and 40 mph ODB test protocols.

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Figure 4-33. VPI for the Accord Targets over Impact Speed

For the Accord lightweight concept, there is substantially more intrusion at the foot rest and

toe-pan areas, as shown in Figure 4-34. This indicates a difference in structural design between

the two vehicles in those areas. Dynamic intrusions are readily available from FE analysis and are

provided by EFP at different speed ranges. They can provide valuable insight for vehicle designers in controlling intrusion in the footwell area, a strategy that is useful in vehicle design.

Given a similar vehicle size, one would expect comparable intrusion levels between the baseline and lightweight concept, which is not the case.

Figure 4-34. Accord Target Maximum Dynamic Intrusions in IIHS Offset Deformable Barrier (ODB) Frontal Impacts at the Foot well Area

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Comparing the percentages of total internal energy absorbed by the same substructures in the

Accord lightweight concept as compared to the baseline, shown in Figure 4-35, points to different structural design strategies. Such measures that are easily obtained from simulation and EFP at different speed ranges can give useful insight to vehicle designers.

Figure 4-35. Accord Absorbed Energies for Vehicle Substructures Computed at end of FE Simulations

4.11.2. Findings Highlights- Accord Targets

The increase in societal risk for the Accord LW in the concept lightweight fleet, as compared

with the risk for the Accord BL in the baseline fleet, is not only due to the Accord weight

decrease and the overall increase in mass ratio of the fleet. Vehicle pulse index and compartment

acceleration pulse overlays indicate that the Accord LW design is somewhat stiffer than the

Accord BL design, especially at the lower speed ranges. The Accord LW FEM was developed to

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match the Accord baseline responses at the regulation speed with no consideration of

performance at the lower speed ranges.

Note that the same occupant environment and restraint system is being used for both the

Accord baseline and lightweight concept vehicles.

4.11.3. Venza Targets Comparisons

The Venza driver responses in the two-vehicle full engagement frontal self-impact are presented in Table 4-45 through Table 4-50 for the midsize male and small female driver in both the baseline and lightweight concepts.

Table 4-45. Venza Baseline Full Engagement Impact- Midsize Male Driver Responses

HIII 50th %ile Dummy Combined Chest Neck Chest Neck Combined Speed Femur HIC15 Femur Injury Risk II Crash Configuration HIC15 Deflectio Tension Deflectio Tension Injury Risk (mph) Max (N) Risk (%) Max (%) (No Femur) n (mm) (N) n (%) (T)(%) (%) (%) 15 30 25 1181 647 0.0% 2.6% 0.3% 0.0% 2.9% 2.6% 20 124 26 1386 1153 0.0% 3.0% 0.4% 0.0% 3.4% 3.1% Venza BL Full 25 141 26 1445 1161 0.0% 3.1% 0.4% 0.0% 3.5% 3.1% 30 186 28 1556 1255 0.1% 3.5% 0.4% 0.0% 4.1% 3.7% 35 263 31 1597 1493 0.6% 5.4% 0.5% 0.1% 6.4% 5.9%

Table 4-46. Venza HO Full Engagement Impact- Midsize Male Driver Responses

HIII 50th %ile Dummy Chest Neck Chest Neck Combined Speed Femur HIC15 Risk Femur Combined Crash Configuration HIC15 Deflection Tension Deflection Tension Injury Risk II (mph) Max (N) (%) Max (%) Injury Risk (%) (mm) (N) (%) (T)(%) (No Femur) (%) 15 41 26 1421 643 0.0% 2.8% 0.4% 0.0% 3.2% 2.8% 20 95 27 1438 938 0.0% 3.2% 0.4% 0.0% 3.6% 3.2% Venza HO Full 25 157 28 1719 1096 0.1% 3.9% 0.5% 0.0% 4.5% 4.0% Engagement 30 230 31 1900 1542 0.3% 5.3% 0.6% 0.1% 6.2% 5.7% 35 309 34 2164 1883 1.0% 6.8% 0.7% 0.1% 8.5% 7.9%

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Table 4-47. Venza LO Full Engagement Impact- Midsize Male Driver Responses

Combined Chest Chest Neck Combined Speed Femur Max Neck HIC15 Risk Femur Max Injury Risk Crash Configuration HIC15 Deflection Deflection Tension Injury Risk (mph) (N) Tension (N) (%) (%) II (No (mm) (%) (T)(%) (%) Femur) (%) 15 38.3765 23.757099 1363.18 690.7653 0.0% 2.2% 0.4% 0.0% 2.6% 2.2% 20 118.174 26.599801 1414.424 1077.975 0.0% 3.2% 0.4% 0.0% 3.6% 3.2% Venza LO Full 25 153.329 27.128173 1720.382 1201.668 0.1% 3.4% 0.5% 0.0% 3.9% 3.4% 30 212.427 28.869839 1726.639 1350.744 0.2% 4.1% 0.5% 0.0% 4.9% 4.4% 35 281.214 33.355494 2051.476 1725.136 0.7% 6.6% 0.6% 0.1% 8.0% 7.4%

Table 4-48. Venza Baseline Full Engagement Impact- Small Female Driver Responses

HIII 5th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Injury Risk II HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) Max (N) (%) Max (%) (No Femur) (mm) (N) (%) (T)(%) (%) (%) 15 118 24 2501 810 0.0% 4.4% 1.5% 0.0% 5.9% 4.5% 20 213 27 2775 1095 0.2% 6.2% 1.9% 0.1% 8.3% 6.5% Venza BL Full 25 242 27 2921 1089 0.4% 6.7% 2.0% 0.1% 9.1% 7.2% 30 297 29 2625 1169 0.9% 8.7% 1.7% 0.1% 11.1% 9.6% 35 404 32 2764 1415 2.5% 11.4% 1.8% 0.4% 15.5% 13.9%

Table 4-49. Venza HO Full Engagement Impact- Small Female Driver Responses

HIII 5th %ile Dummy Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Combined HIC15 Deflection Tension Deflection Tension Injury Risk II Configuration (mph) Max (N) (%) Max (%) Injury Risk (%) (mm) (N) (%) (T)(%) (No Femur) (%) 15 130 23 2704 775 0.0% 3.7% 1.8% 0.0% 5.5% 3.8% 20 256 28 2927 1157 0.5% 7.6% 2.0% 0.1% 10.0% 8.1% Venza HO Full 25 400 31 3133 1440 2.4% 10.0% 2.3% 0.4% 14.6% 12.5% Engagement 30 463 33 2696 1551 3.8% 12.7% 1.8% 0.6% 17.9% 16.5% 35 481 34 2570 1603 4.2% 14.1% 1.6% 0.7% 19.7% 18.3%

Table 4-50. Venza LO Full Engagement Impact- Small Female Driver Responses

HIII 5th %ile Dummy Combined Chest Chest Neck Combined Speed Femur Max Neck HIC15 Risk Femur Max Injury Risk Crash Configuration HIC15 Deflection Deflection Tension Injury Risk (mph) (N) Tension (N) (%) (%) II (No (mm) (%) (T)(%) (%) Femur) (%) 15 83.0792 20.833795 2508.542 604.5737 0.0% 2.8% 1.6% 0.0% 4.3% 2.8% 20 232.551 27.475852 2923.442 1141.953 0.3% 6.8% 2.0% 0.1% 9.2% 7.3% Venza LO Full 25 272.068 27.602436 2928.253 1206.678 0.6% 6.9% 2.0% 0.2% 9.6% 7.7% 30 434.695 31.860092 2826.432 1508.805 3.1% 11.4% 1.9% 0.5% 16.2% 14.6% 35 481.768 33.080269 3218.16 1644.111 4.2% 13.0% 2.4% 0.8% 19.4% 17.4%

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Overall, there was an increase in societal injury risk for both the 50th percentile male and the

5th percentile female drivers in the Venza HO compared with the baseline with an exception at the

lowest speed. This trend was seen across body regions.

• The increases were substantial at the higher speeds, e.g., for the 5th percentile female

driver, there was a slight decrease in the 15 mph full engagement impact, but increases of

20% in the 20 mph and 61% in the 30 mph impacts. The increases for the 50th percentile

dummy ranged from 10% in the 15 mph to 33% in the 35 mph full engagement impacts.

• There were similar but less substantial increases in risk for the Venza LO driver across

body region, as compared with the baseline, with an exception at the lowest speed.

• For the 5th percentile female dummy there was actually a decrease in societal risk of 27%

in the 25 mph impact and an increase of 11% in the 20 mph and up to 25% in the 35 mph

impacts.

• For the 50th percentile dummy, there was actually a decrease in societal risk of 12% in the

25 mph impact and an increase of 6% in the 20mph and up to 25% in the 35 mph

impacts.

4.11.3.1. Venza Compartment Acceleration Pulse Comparison

The compartment acceleration pulse comparisons for the Venza targets are presented in

Figure 4-36. The shorter duration pulses for the Accord lightweight concept indicate a stiffer front-end design than the baseline.

175

Figure 4-36. Venza Baseline versus Lightweight Compartment Pulse Comparisons at 25 and 35 mph

The vehicle pulse severity as demonstrated by VPI is consistently higher for the Venza high

option as compared with the baseline and low option, as shown in Figure 4-37. The VPI for the

lower option, while similar to the baseline at the lower speeds in NCAP, is actually lower than the

baseline in the IIHS ODB configuration. At the higher speeds, the VPI for the low option is

higher than the baseline. The VPI and the compartment pulse comparisons indicate that the

lightweight designs are much stiffer, specifically given the shorter duration acceleration pulse.

The compartment pulses at the lower speeds show similar trends, which are more pronounced for the Venza high option concept design.

Figure 4-37. VPI for the Venza Targets over Impact Speed

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The Venza HO has substantially lower intrusions at the footwell and knee bolster areas than both the baseline and low option design concept, as shown in Figure 4-38 and Figure 4-39. This indicates a much stiffer structure in those vehicle areas. For the low option, intrusions are elevated, especially in the toe-pan area. Given the level of high toe-pan and foot rest intrusions at

40 mph, such a vehicle design will not get a good structural rating in the ODB test protocol.

Figure 4-38. Venza Target Maximum Dynamic Intrusions in IIHS Offset Deformable Barrier (ODB) Frontal Impacts at the Foot well Area

Figure 4-39. Venza Target Maximum Dynamic Intrusions in IIHS Offset Deformable Barrier (ODB) Frontal Impacts at the Knee Bolster

A comparison of the percentages of total internal energy absorbed by the same substructure in the lightweight concepts to the baseline Venza, shown in Figure 4-40, indicates different structural design strategies.

177

Figure 4-40. Venza Absorbed Energies for Vehicle Substructures Computed at end of FE Simulations

4.11.4. Findings Highlights- Venza Targets

The increase in societal risk for both Venza lightweight concepts in the lightweight fleets, as compared with the risk for the Venza BL in the baseline fleet, is not only due to the concepts’ weight decreases and the overall increase in mass of the fleets. For both the LO and HO lightweight concept vehicles, the vehicle pulse index and compartment acceleration pulse overlays indicate their designs are somewhat stiffer than the Venza baseline design, over all the speeds ranges for the HO and at the higher speeds for the LO. The data indicates differences in structural designs and performance between the Venza baseline and the corresponding concept lightweight designs.

Note that the same occupant environment and restraint system are used for both the baseline

and lightweight concepts in both the baseline and LW fleets. 178

4.11.5. Societal Risk Trends over BES for the Baseline and Lightweight Fleets

There is a slight shift in the LW1 fleet and a bit more pronounced shift for the LW2 fleet in

the societal injury risk in the middle speed ranges, as compared to the baseline fleet, shown in

Figure 4-41.

Figure 4-41. SIR over BES for Baseline and Lightweight Fleets

Overall, the short statured drivers, as represented by the 5th percentile dummy, are

overrepresented in the societal risk, with 30-45% of the serious injuries at the different speed ranges, as shown in Figure 4-42. There is an increase in contribution of the short statured driver to societal risk for the both LW1 and LW2 fleets for the CUV segments at the middle speed ranges as compared with the CUV segment in the baseline fleet.

179

Figure 4-42. Contribution of Small Female Driver over BES to SIR for Baseline and Lightweight Fleets

There an increase in the contribution of head injuries to societal risk in the higher speed ranges for both the PC and CUV segments in the LW1 and LW2 (more pronounced for the CUV segment) fleets as compared to the baseline fleet, as shown in Figure 4-43. The contribution of chest injury to societal risk is similar in the fleets across speed ranges with a slight decrease for both the PC and CUV segments at the higher speeds in both the LW1 and LW2 fleets, as shown in Figure 4-44. There is an increase in the contribution of femur injuries for the PC segment in the

17-27 mph speed range in both the LW1 and LW2 fleets, but a decrease in higher speed ranges,

27-40 mph, for both segments in the LW2 fleet, as shown in Figure 4-45. There is an increase in the contribution of femur injuries to the societal injury risk for the CUV segment in the LW1 and

LW2 fleets at speeds less than 17 mph, but a substantial decrease in the LW2 fleet at the high speed ranges of 32-40mph.

180

Figure 4-43. Contribution of Head Injury to SIR in Baseline and Lightweight Fleets

Figure 4-44. Contribution of Chest Injury to SIR in Baseline and Lightweight Fleets

181

Figure 4-45. Contribution of Femur Injury to SIR in Baseline and Lightweight Fleets

182

Chapter 5. Conclusions

A new and operative method for evaluating overall crash safety for vehicles using crash

simulation has been developed and demonstrated for frontal crash modes. EFP, a methodology

for Evaluating Fleet, i.e., self and partner, Protection, has been developed using finite element analysis and rigid body dynamics, in combination with real-world crash and test data, to evaluate self-protection (i.e., crashworthiness), partner-protection (i.e., compatibility), and fleet effects of vehicle designs. The fleet societal risk formulated in this research is a new safety performance measure for crash involved occupants and takes into account the safety of occupants in a target vehicle and the occupants of other vehicles with which it collides in the vehicle fleet across a

range of impact speeds and crash configurations.

The EFP methodology is a novel framework integrating computer simulations and real world

crash analyses. The EFP approach bridges a gap between a computationally complex problem and

a real world application, with an underlying hypothesis that the process is grounded in the

physical world with sufficient granularity in the various components of the methodology.

The initial implementation of the EFP methodology to frontal crashes through proof-of-

concept and case study applications demonstrated the methodology’s capability to evaluate the

crash safety of an existing or new vehicle design in different crash configurations and speeds

representing the real world. Changes in overall societal, target, or partner injury risk between

baseline and modified vehicle designs can be established and evaluated to guide future safety

research efforts. With further development, EFP can be extended to include other crash modes

like side and rear impacts.

The EFP methodology, and the fleet model developed as an initial implementation to frontal

crashes, can be potentially used by vehicles designers to ensure that a new vehicle design lowers 183

the societal risk in the fleet. Policymakers can use such a fleet model to drive future vehicle safety and identify areas of future research.

EFP advances the state of the art of systems modeling in crash safety simulation and addresses limitations identified of the previous system modeling efforts. This method can serve as a powerful tool to assess and introduce particular designs in the fleet and make corresponding decisions on the societal level.

5.1. EFP Insights, Limitations, and Potential Refinements

5.1.1. Safety Insights from Initial Application of Methodology

1. Unlike existing simulation and physical testing approaches which focus only on

occupants of the target vehicle in single vehicle configurations, EFP defines the fleet

societal risk for crash involved occupants as the total safety of occupants in both the

target vehicle and crash partner vehicles. The societal injury risk is an aggregate of

individual crash injury risks weighted by real-world frequency of occurrence of a crash

incident summed over representative impact speeds, crash partners, crash configurations,

occupant sizes, and occupant seating locations.

2. The fleet was represented by four vehicles based on vehicle class and mass—light and

heavy passenger cars, and light and heavy Light Trucks [Sport Utility Vehicles (SUVs)

and Pickups]—to address anticipated changes in the vehicle fleet. This approach is

extensible and provides a more comprehensive inclusion of fleet representation than prior

fleet safety studies.

3. Utilizing the Taurus vehicle models, EFP was able to isolate the effect of mass and

stiffness changes in vehicle designs on total societal injury risk. This could not have been

achieved by physical experimentation. 184

4. EFP identifies suitability of injury risk functions as compared with the real world (NASS

CDS). This was illustrated by the implementation of an alternate chest injury risk

function as a case study. The results were a different but more realistic contribution of

chest injury risk to the overall societal risk, by crash configuration and body regions over

the simulated impact speeds.

5. EFP is able to identify safety effects resulting from fleet composition changes. This was

illustrated by several “what-if” scenarios case studies. For example, in the case study

were the lighter weight car was removed from the fleet and all cars were represented by

the heavier car in the fleet, the societal risk decreased for all the target vehicles. However,

in the case study where the heavier weight car was removed from the fleet and all cars

were represented by the lighter car in the fleet, the societal risk increased for the two

lighter weight car targets but decreased for the lighter CUV concept target.

6. EFP allows the safety evaluation of a vehicle at different crash configurations and speeds

representing the real world. For example, EFP highlighted the importance of vehicle

crashworthiness at the lower speeds that are predominant in the real world as compared

with the traditional speeds at which vehicles are tested for regulatory compliance and

consumer information.

7. EFP identifies contribution to injury risk by occupant sizes: overall and over speed

ranges. Although 25% of drivers are modeled by the small size 5th percentile female

dummy, they accounted for 30-50% of the serious injuries at the lower speed ranges. As

such, EFP highlighted that small statured drivers are overrepresented in the fleet societal

risk for the baseline and lightweight design concepts targets evaluated in this research.

Essentially, EFP highlighted the importance of restraint systems designed to perform well

across the range of occupant sizes. 185

8. EFP and the implemented societal injury risk computation approach provide safety

insights by body region, impact speeds, crash partner, occupant size, and crash

configuration. This was illustrated in both the proof-of-concept and application to

lightweight vehicle concept designs.

9. The EFP overall societal injury risk is based on a computation of a combined risk of

injury to multiple body regions, rather than separately for the head or chest as was used in

existing fleet studies. This provides a more comprehensive injury measure for the

occupant, which accounts for the important body regions in the societal risk, and

facilitates comparison with overall serious injury rates from the field.

10. The EFP formulation allows evaluation of self-protection in single- and two-vehicle

crashes. EFP also allows concurrent evaluation of partner protection in two-vehicle

crashes at multiple configurations and speeds. The two-vehicle crashes evaluate changes

due to vehicle weight (delta-V) and changes due to stiffness (peak G) that are not

evaluated in regulatory and consumer information testing protocols. Although such

protocols are representative of real-world crash configurations, they are impacts into

fixed objects and do not capture vehicle-to-vehicle interactions.

11. EFP highlighted the importance of accounting for both compartment accelerations and

intrusions for better prediction of occupant injury risks. EFP also provides a framework

for assessing effects of both in a virtual environment.

Overall, in addition to providing an operative method for computation of societal injury risk for a target vehicle, EFP contributes to the body of knowledge for vehicle crash safety by highlighting the areas that need improvement and further research. EFP results highlight the need to investigate improved frontal injury risk functions in future studies, such as a better fidelity lower extremity injury risk function, in particular at the higher speeds and better fidelity chest 186

injury risk function at the lower speeds which is exacerbated by the reduced sensitivity of the

available dummies at the lower speeds. EFP results also highlight the need for detailed investigation of the real-world accident (or field) data and the need for improved restraints to better address serious head injuries occurring in the field.

It is important to note that the restraint systems in the occupant models in this application of

EFP were not optimized for the baseline or design variant vehicles. The design variants met or far

exceeded the frontal regulations currently in effect, and yielded simulation results that would

have garnered good ratings from the IIHS moderate overlap and NHTSA’s NCAP frontal testing.

However, current regulation and consumer information testing does not require a manufacturer to

design a vehicle for optimum performance across all speeds and against all partner vehicles and

objects. Simulation studies based on the EFP approach could lead to the development of

strategies for further reduction of societal injury risks across all speeds and objects contacted.

5.1.2. Field Data Analyses Findings and Insights

1. A new and consistent method to identify and trim overly influential NASS CDS case

weights was developed effectively applied in the analysis of the frontal crash population

for the EFP implementation to frontal impacts. Highly influential CDS weights distort the

estimates of involvement and injury distributions and rates in crashes. The new method is

based on the statistics of the mean case weight by vehicle class and injury level, and is in

accordance with the NASS CDS sampling scheme at the case selection level.

2. The frontal crash configurations for fleet model simulation are based on real-world crash

exposure and structural interactions from real-world distributions for both single- and

two-vehicle crashes in the National Automotive Sampling System (NASS)

Crashworthiness Data System (CDS). Frontal crashes were classified in a way that has

187

not been done before, by incorporating corner impacts. Small overlap crashes with side

damage were included as a subset of frontal corner crashes and overall taxonomy. The

expanded taxonomy provided a more comprehensive treatment of overall frontal crash

modes and permitted the assessment of relative crash involvement and contribution to

moderate and serious frontal crash injury of the Full Engagement, Offset, Between Rails,

and Corner frontal crash modes. The predominant crash modes, in terms of the largest

population of seriously and moderately injured drivers, are the Full Engagement and

Offset crash modes.

3. For all body regions studied, the majority of all serious and moderate injuries occurred in

Full Engagement and Offset crash modes for both the younger and older drivers. In

contrast, a much smaller proportion of serious and moderate injuries occur in Corner

impacts, which have been the focus of recent research and testing programs.

4. Frontal crash exposure was established to be consistent across the age groups that were

modeled. The finding of increased fragility of the older population adds to the body of

knowledge by previous researchers.

5.1.3. CAE Process Insights from Methodology

1. The current vehicle structural model constructs utilizing finite element analysis have been

proven to be sufficiently useful and robust to be used to simulate the field accident data in

a virtual environment.

2. In EFP, the safety of new concept vehicle designs and reversed engineered vehicles is

based on occupant loading and responses rather than structural responses in the

simulations of crash configurations of interest. This is contrast to the state of the art of

safety assessment of concept vehicle designs from computer simulations which is based

188

on structural measures and not directly based on occupant responses, as the vehicle

interior and occupant environments are not currently modeled at the vehicle concept

design stage. In this research, the occupant modeling was decoupled from the vehicle

structural modeling and the occupant environments were developed from state-of-art

restraints systems. The occupant simulations were performed separately but were driven

by the structural pulses and intrusions from the vehicle structural simulations.

3. Restraint systems are shown to be critical in evaluating occupant safety in a virtual

environment.

4. A general framework for developing and validating vehicle and occupant models for fleet

simulations was established in two parts: first, model development, followed by model

verification and robustness simulations, both in the crash configurations and impact

speeds of interest.

5.1.4. Limitations of Current Implementation of EFP

The main limitation for the frontal implementation of EFP is the lack of availability of newer fleet vehicle FE models. The current FEMs representing the fleet span model years 2001-2012; thus the results are more representative of transitional fleet safety effects. As more vehicle models become available, they can replace the current ones utilized. In addition, the current vehicle interiors are mainly generic in nature. More detailed and improved characterization of the interior components and restraint systems will result in better model intrusions and occupant interactions with the vehicle interior.

Finite element models of new or modified designs, including lightweight and/or new structural architecture and powertrain designs, are needed to further demonstrate the capability of

EFP to discriminate amongst vehicle designs and evaluate corresponding fleet safety effects in

189

frontal crashes. It is expected that EFP will be able to discriminate between vehicle designs, if the design changes affect the crash pulse, intrusions, restraint systems, and/or occupant environment.

The developed methodology is sensitive enough to detect changes in vehicle design as long as changes in the crash pulse and or intrusions into the occupant compartment are known.

Further calibration of the EFP with real world vehicle safety performance would be useful in future studies. In particular, establishing a direct correlation of the fleet simulations for existing vehicle models could provide baseline error measures for future applications of the methodology.

5.1.5. Potential Refinements of EFP Frontal Implementation 1. Availability of improved occupant models:

a. Incorporate steering column and A-pillar intrusion inputs into occupant model.

b. Develop and incorporate advanced airbag and belt strategies such as adaptive systems

and other advancements in restraint technology (e.g., inflatable belt systems and

advanced load limiting strategies).

c. Perform checks of the “5-30” airbag firing guideline against actual firing times

recorded in available frontal offset and pole crash tests. Investigate obtaining airbag

firing times from real-world EDR data, which could provide the firing time for both

the first and second deployment stages as a function of crash mode, delta-V, and

occupant position.

2. Availability of FEMs of additional vehicle segments to evaluate a broader cross-section

of the vehicle fleet: compact car segment, Hybrid Electric and/or Electric; and over 8,500

lb. GVWR trucks. Also, including FEMs of newer and higher volume midsize car and

small car in the fleet as those become available.

190

3. Currently, EFP provides useful safety performance trends and insights between different

target vehicles (e.g., baseline and redesigned vehicles). Establishing more direct

correlation between fleet model and real-world crash data will make EFP a more

powerful tool. Further analysis of the crash environment and verification of predicted risk

could be performed.

4. Investigate combining the occupant and the vehicle structure in the same simulation

environment, e.g., finite element. This integrated modeling approach would have the

potential of increased efficiency and robustness of EFP.

5.2. Potential Applications of Current EFP

In its current implementation, EFP can be effectively utilized as a tool for many potential applications.

1. EFP can be applied to evaluate both structural and restraint countermeasures that further

reduce societal risk in frontal crashes. On the structural side, analysis of the effects of

structural simulation outputs on corresponding societal risk could provide valuable input

on which front end designs provide improved societal benefits. On the restraint systems

side, EFP provides a virtual tool to evaluate advanced restraint concepts and strategies.

2. EFP provides a tool to evaluate the effects of proportional changes in the fleet mix, e.g.,

effect of the expected increase/decrease in any vehicle segment.

3. EFP provides a tool to evaluate the effects of fleet changes, e.g., the effect of the

interaction of new designs with each other, e.g., interaction between light-weighted

vehicles.

191

4. EFP provides a tool to evaluate the effects demographic changes, i.e., evaluate the effect

of the U.S. aging population. This will require the introduction of appropriate injury risk

functions and examination of the applicability of current dummies.

5. EFP can serve as a new tool to evaluate advanced safety concepts for frontal crash

configurations and determine the corresponding net societal benefits. The following are

some suggested concepts for consideration:

a. Effects of pre-impact braking

b. Effects of external inflatable structures or any pre-crash adaptation of the structure

c. Adaptive restraints

6. EFP can be used to evaluate advanced restraints for right front passengers.

7. EFP can be used to study rear seat safety both relative to crash exposure, as addressed in

the weighting factors, and restraint system, as addressed by the occupant modeling.

8. EFP can be used to evaluate active/passive integration, examples such occupant pre-

position, firing airbag before contact, and changing structural characteristic before

contact.

9. EFP can be applied to estimate societal effects from predicted changes in crash

topologies, for example, changes due to effectiveness of crash avoidance technologies.

New technologies, e.g., automatic emergency braking, could produce different impact

speed distributions in future fleets. Likewise, as Lane Departure Warning and automated

lane-keeping systems are more widely deployed, lower incidence of single-vehicle, road

departure crashes into fixed objects, e.g., poles, would potentially result. Once

established by the safety community, effectiveness estimates of new crash avoidance

technologies could be used to predict the crash environment in the future.

192

5.3. Potential Expansions of EFP

With further development, the EFP methodology can be extended to apply to multiple crash environments, e.g., side and rear impacts.

193

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Appendix A

*SAS version 9.3;

%MACRO SIDECASES(inset,outset);

DATA &outset; SET &inset; IF (year GE 1995) AND (GAD1 IN('L' 'R')) AND (DOF1 IN(11 12 1)) AND (mvehtype NE "X") AND (modelyr GE 1985) AND ((rollover EQ 0) AND (tdd1 NOT EQ "O") AND (tdd2 NOT EQ "O")) AND EXTENT2 IN(. 1 2) AND (dvd NOT EQ .) AND (dvl GT 0) AND (wheelbas GT 0) AND (dirdamw GT 0) THEN DO; * Determine location of front edge of damage relative to estimated vehicle front edge: DIRDAMW is the direct damage width and DVD is the location of direct damage relative to the centerline of the vehicle; frontedge=dvd+(dirdamw/2); frontout40=(wheelbas/2)+(wheelbas*.40); frontout36=(wheelbas/2)+(wheelbas*.36); frontout32=(wheelbas/2)+(wheelbas*.32); frontout28=(wheelbas/2)+(wheelbas*.28); frontout24=(wheelbas/2)+(wheelbas*.24); frontout20=(wheelbas/2)+(wheelbas*.20); frontout16=(wheelbas/2)+(wheelbas*.16); frontout08=(wheelbas/2)+(wheelbas*.08); frontout04=(wheelbas/2)+(wheelbas*.04); frontoutz=(wheelbas/2);

SELECT; WHEN (frontedge GE frontout36) FIT="SideFront36"; WHEN (frontedge GE frontout32) FIT="SideFront32"; WHEN (frontedge GE frontout28) FIT="SideFront28"; WHEN (frontedge GE frontout24) FIT="SideFront24"; WHEN (frontedge GE frontout20) FIT="SideFront20"; WHEN (frontedge GE frontout16) FIT="SideFront16"; WHEN (frontedge GE frontout08) FIT="SideFront08"; WHEN (frontedge GE frontout04) FIT="SideFront04"; WHEN (frontedge GE frontoutz) FIT="SideFront00"; OTHERWISE FIT="Other(Side)"; END; END; 204

IF FIT="" THEN FIT="Unclassified"; RUN; %MEND SIDECASES;

205

Appendix B

Table B1. BAIS3+ population in MY 2000-2011 vehicles (un-weighted NASS CDS sample)

Driver Upper Lower Head Neck Thorax Abdomen Spine NASS Sample Extr Extr 16 ≤ Age ≤ 50 44 9 63 24 3 62 153 Age > 50 31 17 74 13 6 29 68

Table B2. Single Vehicle Population Samples: Young Driver

Full Engagement - Young Offset Frontal - Young Between Rail Frontal- Young Passenger Passenger Passenger Passenger Passenger Passenger BES (mph) Car Car BES (mph) Car Car BES (mph) Car Car ≥3106lb <3106lb ≥3106lb <3106lb ≥3106lb <3106lb 0-12 48 63 0-12 36 57 0-12 22 21 12-17 30 42 12-17 20 36 12-17 19 31 17-22 20 22 17-22 12 9 17-22 18 46 22-27 8 20 22-27 6 11 22-27 21 29 27-32 2 8 27-32 5 14 27-32 8 13 ≥32 6 6 ≥32 12 15 ≥32 16 30 Total 114 161 Total 91 142 Total 104 170

Table B3. Single Vehicle Crashes Expanded Population Samples: all Drivers (Age ≥ 16)

Full Engagement - All Ages Offset Frontal - All Ages Between Rail Frontal- All Ages Passenger Passenger Passenger Passenger Passenger Passenger BES (mph) Car Car BES (mph) Car Car BES (mph) Car Car ≥3106lb <3106lb ≥3106lb <3106lb ≥3106lb <3106lb 0-12 65 70 0-12 53 72 0-12 31 22 12-17 38 48 12-17 24 37 12-17 29 42 17-22 23 28 17-22 16 13 17-22 25 55 22-27 12 21 22-27 9 14 22-27 32 34 27-32 5 9 27-32 6 18 27-32 16 15 ≥32 8 9 ≥32 16 16 ≥32 23 33 Total 151 185 Total 124 170 Total 156 201

206

Appendix C

Table C1 – Baseline Taurus 5th Percentile Occupant Results in Single-Vehicle Crashes

207

Table C2. Partner Vehicle 50th Percentile Occupant Results in Two-Vehicle Crashes with Baseline Taurus Target

TAURUS BASELINE PARTNER VEHICLE OCCUPANT HIII 50th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur Max HIC15 Risk Femur Max Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) (N) (%) (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 14 23 2449 475 0.0% 2.0% 0.8% 0.0% 2.8% 2.0% 20 38 28 3322 622 0.0% 3.7% 1.3% 0.0% 5.0% 3.7% Explorer Full 25 87 29 3642 954 0.0% 4.2% 1.5% 0.0% 5.6% 4.2% 30 173 29 4236 1274 0.1% 4.2% 2.0% 0.0% 6.2% 4.3% 35 244 30 5023 1431 0.4% 4.7% 2.7% 0.1% 7.7% 5.1% 15 9 16 2001 465 0.0% 0.7% 0.6% 0.0% 1.4% 0.7% 20 14 18 2188 515 0.0% 1.0% 0.7% 0.0% 1.7% 1.0% Explorer Offset 25 42 21 2620 570 0.0% 1.5% 0.9% 0.0% 2.4% 1.5% 30 54 27 3227 864 0.0% 3.3% 1.2% 0.0% 4.5% 3.3% 35 115 29 3796 1061 0.0% 4.2% 1.6% 0.0% 5.8% 4.2% 15 10 22 343 393 0.0% 1.8% 0.1% 0.0% 1.8% 1.8% 20 38 27 643 496 0.0% 3.3% 0.2% 0.0% 3.5% 3.3% Silverado Full 25 99 29 1681 667 0.0% 4.2% 0.5% 0.0% 4.7% 4.2% 30 171 30 2612 794 0.1% 4.7% 0.9% 0.0% 5.6% 4.8% 35 221 30 2717 939 0.3% 4.7% 1.0% 0.0% 5.8% 4.9% 15 19 21 63 304 0.0% 1.5% 0.0% 0.0% 1.6% 1.5% 20 32 23 181 439 0.0% 2.0% 0.0% 0.0% 2.1% 2.0% Silverado Offset 25 83 24 254 708 0.0% 2.3% 0.1% 0.0% 2.4% 2.3% 30 126 26 390 869 0.0% 2.9% 0.1% 0.0% 3.1% 3.0% 35 166 28 756 1074 0.1% 3.7% 0.2% 0.0% 4.0% 3.8% 15 62 22 3032 703 0.0% 1.8% 1.1% 0.0% 2.9% 1.8% 20 89 24 3655 766 0.0% 2.3% 1.5% 0.0% 3.8% 2.3% Yaris Full 25 97 24 3717 894 0.0% 2.3% 1.6% 0.0% 3.8% 2.3% 30 139 25 4325 1097 0.0% 2.6% 2.1% 0.0% 4.7% 2.6% 35 286 27 7696 1444 0.8% 3.3% 7.1% 0.1% 10.9% 4.1% 15 23 19 463 381 0.0% 1.2% 0.1% 0.0% 1.3% 1.2% 20 57 23 551 654 0.0% 2.0% 0.1% 0.0% 2.1% 2.0% Yaris Offset 25 100 27 1705 1133 0.0% 3.3% 0.5% 0.0% 3.8% 3.3% 30 295 29 2978 1160 0.9% 4.2% 1.1% 0.0% 6.1% 5.0% 35 469 29 4353 1188 3.9% 4.2% 2.1% 0.0% 9.9% 8.0% 15 27 20 1078 605 0.0% 1.3% 0.3% 0.0% 1.6% 1.3% 20 66 23 1918 920 0.0% 2.0% 0.6% 0.0% 2.6% 2.0% Taurus Full 25 111 24 2827 1202 0.0% 2.3% 1.0% 0.0% 3.3% 2.3% 30 132 26 4334 1254 0.0% 2.9% 2.1% 0.0% 5.0% 3.0% 35 163 26 5124 1349 0.1% 2.9% 2.8% 0.0% 5.8% 3.0% 15 12 17 254 429 0.0% 0.9% 0.1% 0.0% 0.9% 0.9% 20 21 19 224 624 0.0% 1.2% 0.1% 0.0% 1.2% 1.2% Taurus Offset 25 47 22 559 781 0.0% 1.8% 0.1% 0.0% 1.9% 1.8% 30 90 24 1634 1043 0.0% 2.3% 0.5% 0.0% 2.8% 2.3% 35 194 26 2471 1513 0.2% 2.9% 0.8% 0.1% 4.0% 3.1% 208

Table C3. Baseline Taurus 5th Percentile Occupant Results in Two-Vehicle Crashes

TAURUS BASELINE TARGET VEHICLE OCCUPANT HIII 5th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur Max HIC15 Risk Femur Max Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) (N) (%) (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 171 23 1887 1437 0.1% 3.8% 1.0% 0.4% 5.2% 4.2% 20 186 24 1998 1475 0.1% 4.3% 1.1% 0.5% 5.9% 4.9% Explorer Full 25 210 26 2048 1605 0.2% 5.7% 1.1% 0.7% 7.6% 6.6% 30 204 27 2663 1398 0.2% 6.4% 1.7% 0.3% 8.5% 6.9% 35 209 28 3040 1476 0.2% 7.3% 2.2% 0.5% 9.9% 7.9% 15 152 21 2181 1445 0.1% 2.8% 1.2% 0.4% 4.5% 3.3% 20 170 24 2185 1615 0.1% 4.3% 1.2% 0.8% 6.3% 5.2% Explorer Offset 25 203 26 2323 1833 0.2% 5.7% 1.4% 1.7% 8.7% 7.5% 30 199 27 1985 1687 0.2% 6.4% 1.1% 1.0% 8.5% 7.5% 35 220 28 1813 1707 0.3% 7.3% 0.9% 1.1% 9.4% 8.5% 15 174 22 2009 1442 0.1% 3.3% 1.1% 0.4% 4.8% 3.8% 20 179 24 1936 1423 0.1% 4.3% 1.0% 0.4% 5.8% 4.8% Silverado Full 25 188 26 2236 1441 0.1% 5.7% 1.3% 0.4% 7.4% 6.2% 30 203 27 2634 1445 0.2% 6.4% 1.7% 0.4% 8.6% 7.0% 35 201 27 3301 1366 0.2% 6.4% 2.6% 0.3% 9.3% 6.9% 15 168 22 2134 1605 0.1% 3.3% 1.2% 0.7% 5.2% 4.1% 20 187 25 2168 1675 0.1% 5.0% 1.2% 1.0% 7.2% 6.0% Silverado Offset 25 187 26 2307 1721 0.1% 5.7% 1.4% 1.1% 8.1% 6.9% 30 215 27 2036 1744 0.2% 6.4% 1.1% 1.2% 8.8% 7.8% 35 221 28 2003 1776 0.3% 7.3% 1.1% 1.4% 9.8% 8.8% 15 170 22 2045 1419 0.1% 3.3% 1.1% 0.4% 4.8% 3.7% 20 166 23 1848 1371 0.1% 3.8% 1.0% 0.3% 5.1% 4.1% Yaris Full 25 184 25 2046 1418 0.1% 5.0% 1.1% 0.4% 6.5% 5.4% 30 167 25 2390 1340 0.1% 5.0% 1.4% 0.3% 6.7% 5.3% 35 196 26 2469 1477 0.2% 5.7% 1.5% 0.5% 7.7% 6.3% 15 125 14 1585 1276 0.0% 0.9% 0.8% 0.2% 1.9% 1.1% 20 136 18 1732 1401 0.0% 1.8% 0.9% 0.3% 3.0% 2.1% Yaris Offset 25 162 22 2067 1552 0.1% 3.3% 1.1% 0.6% 5.0% 3.9% 30 159 23 2199 1507 0.1% 3.8% 1.3% 0.5% 5.5% 4.3% 35 191 25 2222 1671 0.1% 5.0% 1.3% 0.9% 7.2% 6.0% 15 160 21 1971 1335 0.1% 2.8% 1.1% 0.3% 4.2% 3.2% 20 184 23 1898 1468 0.1% 3.8% 1.0% 0.4% 5.3% 4.3% Taurus Full 25 179 24 2087 1394 0.1% 4.3% 1.2% 0.3% 5.9% 4.8% 30 188 25 2312 1436 0.1% 5.0% 1.4% 0.4% 6.8% 5.5% 35 178 25 2563 1366 0.1% 5.0% 1.6% 0.3% 6.9% 5.4% 15 139 19 2180 1426 0.0% 2.1% 1.2% 0.4% 3.7% 2.5% 20 148 21 2194 1526 0.0% 2.8% 1.2% 0.5% 4.6% 3.4% Taurus Offset 25 163 22 2179 1507 0.1% 3.3% 1.2% 0.5% 5.0% 3.8% 30 158 23 2203 1398 0.1% 3.8% 1.3% 0.3% 5.4% 4.2% 35 178 25 2185 1532 0.1% 5.0% 1.2% 0.6% 6.8% 5.6%

209

Table C4. Partner Vehicle 5th Percentile Occupant Results in Two-Vehicle Crashes with Baseline Taurus Target

TAURUS BASELINE PARTNER VEHICLE OCCUPANT HIII 5th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur Max HIC15 Risk Femur Max Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) (N) (%) (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 60 22 1379 980 0.0% 3.3% 0.6% 0.1% 4.0% 3.3% 20 68 23 1532 1155 0.0% 3.8% 0.7% 0.1% 4.6% 3.9% Explorer Full 25 86 24 1754 1211 0.0% 4.3% 0.9% 0.2% 5.4% 4.5% 30 106 27 1812 1333 0.0% 6.4% 0.9% 0.3% 7.6% 6.7% 35 106 28 1857 1462 0.0% 7.3% 1.0% 0.4% 8.6% 7.7% 15 103 21 1821 1060 0.0% 2.8% 0.9% 0.1% 3.8% 2.9% 20 102 21 1925 1094 0.0% 2.8% 1.0% 0.1% 3.9% 2.9% Explorer Offset 25 81 21 2090 1103 0.0% 2.8% 1.2% 0.1% 4.1% 2.9% 30 72 21 2172 1050 0.0% 2.8% 1.2% 0.1% 4.1% 2.9% 35 98 24 2316 1132 0.0% 4.3% 1.4% 0.1% 5.8% 4.5% 15 62 24 283 895 0.0% 4.3% 0.1% 0.1% 4.5% 4.4% 20 54 27 706 996 0.0% 6.4% 0.3% 0.1% 6.8% 6.5% Silverado Full 25 94 28 983 981 0.0% 7.3% 0.4% 0.1% 7.7% 7.4% 30 169 29 1104 963 0.1% 8.2% 0.5% 0.1% 8.8% 8.4% 35 126 29 887 973 0.0% 8.2% 0.4% 0.1% 8.6% 8.3% 15 66 23 196 821 0.0% 3.8% 0.1% 0.0% 3.9% 3.8% 20 64 24 363 791 0.0% 4.3% 0.1% 0.0% 4.5% 4.4% Silverado Offset 25 59 25 523 880 0.0% 5.0% 0.2% 0.0% 5.2% 5.0% 30 58 26 657 911 0.0% 5.7% 0.3% 0.1% 6.0% 5.7% 35 78 28 746 967 0.0% 7.3% 0.3% 0.1% 7.6% 7.4% 15 212 25 1331 961 0.2% 5.0% 0.6% 0.1% 5.8% 5.3% 20 279 28 1814 1003 0.7% 7.3% 0.9% 0.1% 8.9% 8.0% Yaris Full 25 329 30 2124 1003 1.3% 9.2% 1.2% 0.1% 11.5% 10.5% 30 344 30 2183 1056 1.5% 9.2% 1.2% 0.1% 11.8% 10.7% 35 349 31 2729 1055 1.5% 10.4% 1.8% 0.1% 13.4% 11.8% 15 135 18 575 711 0.0% 1.8% 0.2% 0.0% 2.0% 1.8% 20 170 21 894 863 0.1% 2.8% 0.4% 0.0% 3.3% 3.0% Yaris Offset 25 280 27 1226 1183 0.7% 6.4% 0.5% 0.2% 7.7% 7.2% 30 326 28 1586 1259 1.2% 7.3% 0.8% 0.2% 9.3% 8.6% 35 321 28 1785 1130 1.2% 7.3% 0.9% 0.1% 9.3% 8.5% 15 160 21 1971 1335 0.1% 2.8% 1.1% 0.3% 4.2% 3.2% 20 184 23 1898 1468 0.1% 3.8% 1.0% 0.4% 5.3% 4.3% Taurus Full 25 179 24 2087 1394 0.1% 4.3% 1.2% 0.3% 5.9% 4.8% 30 188 25 2312 1436 0.1% 5.0% 1.4% 0.4% 6.8% 5.5% 35 178 25 2563 1366 0.1% 5.0% 1.6% 0.3% 6.9% 5.4% 15 139 19 2180 1426 0.0% 2.1% 1.2% 0.4% 3.7% 2.5% 20 148 21 2194 1526 0.0% 2.8% 1.2% 0.5% 4.6% 3.4% Taurus Offset 25 163 22 2179 1507 0.1% 3.3% 1.2% 0.5% 5.0% 3.8% 30 158 23 2203 1398 0.1% 3.8% 1.3% 0.3% 5.4% 4.2% 35 178 25 2185 1532 0.1% 5.0% 1.2% 0.6% 6.8% 5.6% 210

Table C5. Taurus_LW 50th Percentile Occupant Results in Single-Vehicle Crashes

Table C6. Taurus_LW 5th Percentile Occupant Results in Single-Vehicle Crashes

211

Table C7. Taurus_LW 50th Percentile Occupant Results in Two-Vehicle Crashes

TAURUS_LW TARGET VEHICLE OCCUPANT HIII 50th %ile Dummy Combined Chest Chest Neck Combined Speed Femur Max Neck HIC15 Risk Femur Max Injury Risk Crash Configuration HIC15 Deflection Deflection Tension Injury Risk (mph) (N) Tension (N) (%) (%) II (No (mm) (%) (T)(%) (%) Femur) (%) 15 83 24 2060 1030 0.0% 2.3% 0.6% 0.0% 2.9% 2.3% 20 135 25 3774 1245 0.0% 2.6% 1.6% 0.0% 4.2% 2.7% Explorer Full 25 179 27 6739 1428 0.1% 3.3% 5.1% 0.1% 8.4% 3.5% 30 316 28 6949 1909 1.1% 3.7% 5.5% 0.2% 10.2% 4.9% 35 389 28 6733 1941 2.2% 3.7% 5.1% 0.2% 10.8% 6.0% 15 21 18 306 563 0.0% 1.0% 0.1% 0.0% 1.1% 1.0% 20 59 24 979 1007 0.0% 2.3% 0.3% 0.0% 2.6% 2.3% Explorer Offset 25 110 25 2818 1330 0.0% 2.6% 1.0% 0.0% 3.6% 2.6% 30 195 27 3852 1539 0.2% 3.3% 1.7% 0.1% 5.1% 3.5% 35 492 28 4596 1859 4.5% 3.7% 2.3% 0.1% 10.3% 8.2% 15 58 23 2065 918 0.0% 2.0% 0.6% 0.0% 2.7% 2.0% 20 134 26 4556 1256 0.0% 2.9% 2.3% 0.0% 5.2% 3.0% Silverado Full 25 170 27 7808 1425 0.1% 3.3% 7.4% 0.1% 10.6% 3.4% 30 300 28 13056 1818 0.9% 3.7% 32.0% 0.1% 35.2% 4.7% 35 382 28 12747 1964 2.1% 3.7% 29.8% 0.2% 33.9% 5.9% 15 44 22 486 934 0.0% 1.8% 0.1% 0.0% 1.9% 1.8% 20 96 25 1697 1190 0.0% 2.6% 0.5% 0.0% 3.1% 2.6% Silverado Offset 25 137 26 2609 1269 0.0% 2.9% 0.9% 0.0% 3.9% 3.0% 30 158 27 3533 1386 0.1% 3.3% 1.4% 0.0% 4.8% 3.4% 35 346 28 5184 1666 1.5% 3.7% 2.9% 0.1% 8.0% 5.2% 15 47 23 1891 838 0.0% 2.0% 0.6% 0.0% 2.6% 2.0% 20 101 25 3522 1097 0.0% 2.6% 1.4% 0.0% 4.0% 2.6% Yaris Full 25 137 26 5145 1268 0.0% 2.9% 2.9% 0.0% 5.8% 3.0% 30 162 26 5800 1322 0.1% 2.9% 3.7% 0.0% 6.6% 3.0% 35 203 27 6063 1412 0.2% 3.3% 4.0% 0.0% 7.4% 3.5% 15 6 13 219 310 0.0% 0.4% 0.1% 0.0% 0.5% 0.4% 20 16 16 324 413 0.0% 0.7% 0.1% 0.0% 0.8% 0.7% Yaris Offset 25 50 22 641 916 0.0% 1.8% 0.2% 0.0% 1.9% 1.8% 30 122 24 1569 1188 0.0% 2.3% 0.5% 0.0% 2.8% 2.3% 35 205 25 2411 1555 0.2% 2.6% 0.8% 0.1% 3.6% 2.9% 15 42 22 1720 755 0.0% 1.8% 0.5% 0.0% 2.3% 1.8% 20 111 25 2906 1207 0.0% 2.6% 1.1% 0.0% 3.7% 2.6% Taurus Full 25 132 25 4045 1291 0.0% 2.6% 1.8% 0.0% 4.4% 2.7% 30 159 26 5515 1329 0.1% 2.9% 3.3% 0.0% 6.2% 3.0% 35 239 27 6387 1513 0.4% 3.3% 4.5% 0.1% 8.1% 3.7% 15 26 20 338 680 0.0% 1.3% 0.1% 0.0% 1.4% 1.3% 20 34 21 763 710 0.0% 1.5% 0.2% 0.0% 1.7% 1.5% Taurus Offset 25 82 24 618 981 0.0% 2.3% 0.2% 0.0% 2.5% 2.3% 30 116 25 1848 1201 0.0% 2.6% 0.6% 0.0% 3.2% 2.6% 35 172 26 2714 1561 0.1% 2.9% 1.0% 0.1% 4.0% 3.1%

212

Table C8. Taurus_LW 5th Percentile Occupant Results in Two-Vehicle Crashes

TAURUS_LW TARGET VEHICLE OCCUPANT HIII 5th %ile Dummy Combined Chest Chest Neck Combined Speed Femur Max Neck HIC15 Risk Femur Max Injury Risk Crash Configuration HIC15 Deflection Deflection Tension Injury Risk (mph) (N) Tension (N) (%) (%) II (No (mm) (%) (T)(%) (%) Femur) (%) 15 169 24 1885 1377 0.1% 4.3% 1.0% 0.3% 5.7% 4.7% 20 190 25 2180 1443 0.1% 5.0% 1.2% 0.4% 6.7% 5.5% Explorer Full 25 229 27 2437 1659 0.3% 6.4% 1.5% 0.9% 8.9% 7.6% 30 251 30 2993 1559 0.5% 9.2% 2.1% 0.6% 12.1% 10.2% 35 318 31 3349 1824 1.1% 10.4% 2.6% 1.7% 15.1% 12.8% 15 148 20 2334 1534 0.0% 2.4% 1.4% 0.6% 4.4% 3.0% 20 168 25 2157 1527 0.1% 5.0% 1.2% 0.5% 6.7% 5.6% Explorer Offset 25 207 27 2068 1803 0.2% 6.4% 1.1% 1.5% 9.1% 8.1% 30 241 28 1965 1936 0.4% 7.3% 1.1% 2.5% 10.9% 10.0% 35 243 29 2343 1811 0.4% 8.2% 1.4% 1.6% 11.3% 10.0% 15 184 24 1809 1468 0.1% 4.3% 0.9% 0.4% 5.8% 4.9% 20 191 25 2236 1462 0.1% 5.0% 1.3% 0.4% 6.7% 5.5% Silverado Full 25 215 27 2685 1527 0.2% 6.4% 1.7% 0.5% 8.8% 7.2% 30 226 28 3250 1509 0.3% 7.3% 2.5% 0.5% 10.3% 8.0% 35 311 31 3999 1722 1.0% 10.4% 3.8% 1.1% 15.6% 12.3% 15 168 24 2243 1604 0.1% 4.3% 1.3% 0.7% 6.3% 5.1% 20 185 26 2039 1610 0.1% 5.7% 1.1% 0.7% 7.5% 6.5% Silverado Offset 25 193 27 1950 1645 0.2% 6.4% 1.0% 0.9% 8.3% 7.4% 30 228 28 1995 1788 0.3% 7.3% 1.1% 1.5% 9.9% 8.9% 35 258 30 1967 1860 0.5% 9.2% 1.1% 1.9% 12.4% 11.4% 15 182 23 1834 1459 0.1% 3.8% 0.9% 0.4% 5.2% 4.3% 20 178 24 2191 1383 0.1% 4.3% 1.2% 0.3% 5.9% 4.8% Yaris Full 25 190 26 2461 1440 0.1% 5.7% 1.5% 0.4% 7.6% 6.2% 30 193 26 2714 1398 0.2% 5.7% 1.8% 0.3% 7.8% 6.1% 35 199 27 2834 1413 0.2% 6.4% 1.9% 0.4% 8.7% 6.9% 15 133 16 1717 1227 0.0% 1.3% 0.9% 0.2% 2.3% 1.5% 20 144 17 1878 1350 0.0% 1.5% 1.0% 0.3% 2.8% 1.8% Yaris Offset 25 178 23 2154 1679 0.1% 3.8% 1.2% 1.0% 6.0% 4.8% 30 173 25 2096 1550 0.1% 5.0% 1.2% 0.6% 6.7% 5.6% 35 182 25 2128 1564 0.1% 5.0% 1.2% 0.6% 6.8% 5.7% 15 169 23 1927 1399 0.1% 3.8% 1.0% 0.3% 5.2% 4.2% 20 172 24 2222 1369 0.1% 4.3% 1.3% 0.3% 5.9% 4.7% Taurus Full 25 173 25 2487 1355 0.1% 5.0% 1.5% 0.3% 6.8% 5.3% 30 187 26 2686 1402 0.1% 5.7% 1.7% 0.3% 7.8% 6.1% 35 204 26 2866 1447 0.2% 5.7% 2.0% 0.4% 8.1% 6.2% 15 152 22 2313 1499 0.1% 3.3% 1.4% 0.5% 5.1% 3.8% 20 166 21 2230 1431 0.1% 2.8% 1.3% 0.4% 4.5% 3.3% Taurus Offset 25 162 23 2393 1509 0.1% 3.8% 1.4% 0.5% 5.7% 4.3% 30 182 26 2121 1563 0.1% 5.7% 1.2% 0.6% 7.5% 6.4% 35 182 26 1890 1481 0.1% 5.7% 1.0% 0.5% 7.1% 6.2%

213

Table C9. Partner Vehicle 50th Percentile Occupant Results in Two-Vehicle Crashes with Taurus_LW Target

TAURUS_LW PARTNER VEHICLE OCCUPANT HIII 50th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) Max (N) (%) Max (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 9 22 2264 452 0.0% 1.8% 0.7% 0.0% 2.5% 1.8% 20 36 26 2983 517 0.0% 2.9% 1.1% 0.0% 4.0% 2.9% Explorer Full 25 61 29 3614 804 0.0% 4.2% 1.5% 0.0% 5.6% 4.2% 30 119 29 4198 1183 0.0% 4.2% 1.9% 0.0% 6.1% 4.2% 35 191 29 4416 1337 0.1% 4.2% 2.1% 0.0% 6.4% 4.3% 15 8 15 1119 364 0.0% 0.6% 0.3% 0.0% 0.9% 0.6% 20 10 17 2214 489 0.0% 0.9% 0.7% 0.0% 1.6% 0.9% Explorer Offset 25 26 19 2516 503 0.0% 1.2% 0.9% 0.0% 2.0% 1.2% 30 59 24 2788 612 0.0% 2.3% 1.0% 0.0% 3.3% 2.3% 35 91 29 3455 1031 0.0% 4.2% 1.4% 0.0% 5.5% 4.2% 15 8 21 307 381 0.0% 1.5% 0.1% 0.0% 1.6% 1.5% 20 20 25 444 478 0.0% 2.6% 0.1% 0.0% 2.7% 2.6% Silverado Full 25 72 28 1188 635 0.0% 3.7% 0.3% 0.0% 4.0% 3.7% 30 127 29 2032 697 0.0% 4.2% 0.6% 0.0% 4.8% 4.2% 35 189 29 2180 865 0.1% 4.2% 0.7% 0.0% 5.0% 4.3% 15 15 21 30 334 0.0% 1.5% 0.0% 0.0% 1.5% 1.5% 20 19 22 133 375 0.0% 1.8% 0.0% 0.0% 1.8% 1.8% Silverado Offset 25 78 24 251 612 0.0% 2.3% 0.1% 0.0% 2.4% 2.3% 30 119 25 436 564 0.0% 2.6% 0.1% 0.0% 2.7% 2.6% 35 132 26 672 875 0.0% 2.9% 0.2% 0.0% 3.1% 3.0% 15 50 22 2985 701 0.0% 1.8% 1.1% 0.0% 2.9% 1.8% 20 83 23 3466 731 0.0% 2.0% 1.4% 0.0% 3.4% 2.0% Yaris Full 25 96 24 3878 752 0.0% 2.3% 1.7% 0.0% 4.0% 2.3% 30 106 25 3878 958 0.0% 2.6% 1.7% 0.0% 4.3% 2.6% 35 205 26 6218 1364 0.2% 2.9% 4.3% 0.0% 7.3% 3.2% 15 18 20 451 440 0.0% 1.3% 0.1% 0.0% 1.4% 1.3% 20 49 22 539 654 0.0% 1.8% 0.1% 0.0% 1.9% 1.8% Yaris Offset 25 95 27 1535 1008 0.0% 3.3% 0.4% 0.0% 3.8% 3.3% 30 122 28 1998 1264 0.0% 3.7% 0.6% 0.0% 4.4% 3.8% 35 478 29 3726 1133 4.2% 4.2% 1.6% 0.0% 9.6% 8.2% 15 25 20 1040 599 0.0% 1.3% 0.3% 0.0% 1.6% 1.3% 20 51 23 1766 818 0.0% 2.0% 0.5% 0.0% 2.5% 2.0% Taurus Full 25 106 25 3161 1155 0.0% 2.6% 1.2% 0.0% 3.8% 2.6% 30 126 25 4031 1260 0.0% 2.6% 1.8% 0.0% 4.4% 2.6% 35 146 26 5283 1318 0.0% 2.9% 3.0% 0.0% 5.9% 3.0% 15 13 18 262 477 0.0% 1.0% 0.1% 0.0% 1.1% 1.0% 20 22 19 296 636 0.0% 1.2% 0.1% 0.0% 1.2% 1.2% Taurus Offset 25 35 21 687 756 0.0% 1.5% 0.2% 0.0% 1.7% 1.5% 30 72 23 1210 938 0.0% 2.0% 0.3% 0.0% 2.3% 2.0% 35 140 25 1892 1313 0.0% 2.6% 0.6% 0.0% 3.2% 2.7%

214

Table C10. Partner Vehicle 5th Percentile Occupant Results in Two-Vehicle Crashes with Taurus_LW Target

TAURUS_LW PARTNER VEHICLE OCCUPANT HIII 5th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) Max (N) (%) Max (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 66 23 1373 953 0.0% 3.8% 0.6% 0.1% 4.4% 3.8% 20 63 23 1493 1124 0.0% 3.8% 0.7% 0.1% 4.6% 3.9% Explorer Full 25 85 24 1721 1141 0.0% 4.3% 0.9% 0.1% 5.3% 4.5% 30 111 27 1803 1470 0.0% 6.4% 0.9% 0.4% 7.7% 6.9% 35 101 27 1686 1338 0.0% 6.4% 0.8% 0.3% 7.5% 6.7% 15 70 18 1611 976 0.0% 1.8% 0.8% 0.1% 2.6% 1.8% 20 105 21 1933 1111 0.0% 2.8% 1.0% 0.1% 3.9% 2.9% Explorer Offset 25 90 20 2033 1161 0.0% 2.4% 1.1% 0.1% 3.6% 2.6% 30 79 21 2130 1074 0.0% 2.8% 1.2% 0.1% 4.1% 2.9% 35 75 22 2199 1220 0.0% 3.3% 1.3% 0.2% 4.7% 3.4% 15 65 24 215 952 0.0% 4.3% 0.1% 0.1% 4.5% 4.4% 20 58 25 588 948 0.0% 5.0% 0.2% 0.1% 5.2% 5.0% Silverado Full 25 82 28 854 939 0.0% 7.3% 0.3% 0.1% 7.7% 7.4% 30 118 28 955 1015 0.0% 7.3% 0.4% 0.1% 7.7% 7.4% 35 114 28 936 1012 0.0% 7.3% 0.4% 0.1% 7.7% 7.4% 15 65 22 264 769 0.0% 3.3% 0.1% 0.0% 3.4% 3.3% 20 63 24 285 785 0.0% 4.3% 0.1% 0.0% 4.5% 4.4% Silverado Offset 25 58 24 515 867 0.0% 4.3% 0.2% 0.0% 4.6% 4.4% 30 53 26 625 916 0.0% 5.7% 0.2% 0.1% 5.9% 5.7% 35 69 27 674 901 0.0% 6.4% 0.3% 0.1% 6.7% 6.5% 15 206 25 1219 931 0.2% 5.0% 0.5% 0.1% 5.7% 5.2% 20 263 27 1791 949 0.6% 6.4% 0.9% 0.1% 7.9% 7.0% Yaris Full 25 321 30 1666 1002 1.2% 9.2% 0.8% 0.1% 11.1% 10.4% 30 328 30 2189 1015 1.2% 9.2% 1.2% 0.1% 11.6% 10.5% 35 358 31 2521 1037 1.7% 10.4% 1.6% 0.1% 13.3% 11.9% 15 137 17 419 728 0.0% 1.5% 0.2% 0.0% 1.7% 1.6% 20 161 20 832 811 0.1% 2.4% 0.3% 0.0% 2.9% 2.5% Yaris Offset 25 266 26 1207 1188 0.6% 5.7% 0.5% 0.2% 6.9% 6.4% 30 318 28 1547 1197 1.1% 7.3% 0.7% 0.2% 9.2% 8.5% 35 327 28 1724 1222 1.2% 7.3% 0.9% 0.2% 9.4% 8.6% 15 156 21 1951 1333 0.1% 2.8% 1.0% 0.3% 4.1% 3.1% 20 177 23 1887 1424 0.1% 3.8% 1.0% 0.4% 5.2% 4.2% Taurus Full 25 181 25 2011 1410 0.1% 5.0% 1.1% 0.4% 6.4% 5.4% 30 184 25 2362 1417 0.1% 5.0% 1.4% 0.4% 6.8% 5.4% 35 179 25 2564 1375 0.1% 5.0% 1.6% 0.3% 6.9% 5.4% 15 141 20 2153 1452 0.0% 2.4% 1.2% 0.4% 4.0% 2.9% 20 153 21 2231 1551 0.1% 2.8% 1.3% 0.6% 4.7% 3.5% Taurus Offset 25 165 22 2202 1554 0.1% 3.3% 1.3% 0.6% 5.1% 3.9% 30 160 23 2224 1469 0.1% 3.8% 1.3% 0.4% 5.5% 4.3% 35 165 25 2188 1427 0.1% 5.0% 1.2% 0.4% 6.6% 5.4%

215

Table C11. Taurus_ST 50th Percentile Occupant Results in Single-Vehicle Crashes

Table C12. Taurus_ST 5th Percentile Occupant Results in Single-Vehicle Crashes

216

Table C13. Taurus_ST 50th Percentile Occupant Results in Two-Vehicle Crashes

TAURUS_ST TARGET VEHICLE OCCUPANT HIII 50th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) Max (N) (%) Max (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 66 23 1873 920 0.0% 2.0% 0.6% 0.0% 2.6% 2.0% 20 135 26 3747 1246 0.0% 2.9% 1.6% 0.0% 4.5% 3.0% Explorer Full 25 161 27 6507 1332 0.1% 3.3% 4.7% 0.0% 8.0% 3.4% 30 255 28 11474 1703 0.5% 3.7% 21.8% 0.1% 25.1% 4.3% 35 320 29 12944 1741 1.1% 4.2% 31.2% 0.1% 34.9% 5.4% 15 14 18 464 526 0.0% 1.0% 0.1% 0.0% 1.1% 1.0% 20 66 25 2259 1175 0.0% 2.6% 0.7% 0.0% 3.3% 2.6% Explorer Offset 25 108 27 3606 1391 0.0% 3.3% 1.5% 0.0% 4.8% 3.4% 30 135 28 4484 1598 0.0% 3.7% 2.2% 0.1% 5.9% 3.8% 35 845 28 6197 1678 16.8% 3.7% 4.2% 0.1% 23.3% 19.9% 15 62 24 2324 979 0.0% 2.3% 0.8% 0.0% 3.0% 2.3% 20 133 26 4916 1255 0.0% 2.9% 2.6% 0.0% 5.5% 3.0% Silverado Full 25 174 27 9167 1392 0.1% 3.3% 11.3% 0.0% 14.4% 3.4% 30 203 28 9687 1514 0.2% 3.7% 13.2% 0.1% 16.7% 4.0% 35 375 28 10882 1854 2.0% 3.7% 18.6% 0.1% 23.3% 5.7% 15 70 24 2151 1093 0.0% 2.3% 0.7% 0.0% 3.0% 2.3% 20 100 26 4494 1368 0.0% 2.9% 2.2% 0.0% 5.1% 3.0% Silverado Offset 25 97 26 3973 1360 0.0% 2.9% 1.8% 0.0% 4.7% 3.0% 30 112 26 4501 1400 0.0% 2.9% 2.2% 0.0% 5.1% 3.0% 35 227 27 4141 1365 0.3% 3.3% 1.9% 0.0% 5.5% 3.6% 15 37 22 1647 762 0.0% 1.8% 0.5% 0.0% 2.2% 1.8% 20 65 24 3157 987 0.0% 2.3% 1.2% 0.0% 3.5% 2.3% Yaris Full 25 128 25 4692 1237 0.0% 2.6% 2.4% 0.0% 5.0% 2.6% 30 152 26 5733 1253 0.1% 2.9% 3.6% 0.0% 6.5% 3.0% 35 183 27 9195 1469 0.1% 3.3% 11.4% 0.1% 14.5% 3.5% 15 5 13 321 304 0.0% 0.4% 0.1% 0.0% 0.5% 0.4% 20 21 19 351 604 0.0% 1.2% 0.1% 0.0% 1.2% 1.2% Yaris Offset 25 48 23 968 970 0.0% 2.0% 0.3% 0.0% 2.3% 2.0% 30 87 26 2931 1196 0.0% 2.9% 1.1% 0.0% 4.0% 3.0% 35 129 26 3757 1187 0.0% 2.9% 1.6% 0.0% 4.5% 3.0% 15 31 20 997 642 0.0% 1.3% 0.3% 0.0% 1.6% 1.3% 20 49 23 1814 835 0.0% 2.0% 0.5% 0.0% 2.6% 2.0% Taurus Full 25 122 25 4347 1247 0.0% 2.6% 2.1% 0.0% 4.7% 2.6% 30 171 27 6562 1404 0.1% 3.3% 4.8% 0.0% 8.1% 3.4% 35 214 28 8598 1550 0.2% 3.7% 9.5% 0.1% 13.1% 4.0% 15 13 18 296 515 0.0% 1.0% 0.1% 0.0% 1.1% 1.0% 20 41 21 700 854 0.0% 1.5% 0.2% 0.0% 1.7% 1.5% Taurus Offset 25 74 24 1330 1090 0.0% 2.3% 0.4% 0.0% 2.7% 2.3% 30 111 27 3331 1277 0.0% 3.3% 1.3% 0.0% 4.6% 3.3% 35 135 27 4436 1348 0.0% 3.3% 2.2% 0.0% 5.5% 3.4%

217

Table C14. Taurus_ST 5th Percentile Occupant Results in Two-Vehicle Crashes

TAURUS_ST TARGET VEHICLE OCCUPANT HIII 5th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) Max (N) (%) Max (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 177 23 2012 1423 0.1% 3.8% 1.1% 0.4% 5.3% 4.2% 20 198 25 2163 1472 0.2% 5.0% 1.2% 0.4% 6.7% 5.6% Explorer Full 25 220 27 2551 1563 0.3% 6.4% 1.6% 0.6% 8.8% 7.3% 30 222 29 2964 1543 0.3% 8.2% 2.1% 0.6% 10.9% 9.0% 35 311 31 3340 1789 1.0% 10.4% 2.6% 1.5% 14.9% 12.6% 15 144 20 2601 1492 0.0% 2.4% 1.7% 0.5% 4.5% 2.9% 20 186 26 2335 1703 0.1% 5.7% 1.4% 1.1% 8.1% 6.8% Explorer Offset 25 218 27 2039 1772 0.3% 6.4% 1.1% 1.4% 9.0% 8.0% 30 239 29 2304 1878 0.4% 8.2% 1.4% 2.0% 11.6% 10.4% 35 258 29 2435 1953 0.5% 8.2% 1.5% 2.7% 12.5% 11.1% 15 175 24 1880 1398 0.1% 4.3% 1.0% 0.3% 5.7% 4.8% 20 188 25 2405 1428 0.1% 5.0% 1.4% 0.4% 6.8% 5.5% Silverado Full 25 227 27 2849 1538 0.3% 6.4% 1.9% 0.6% 9.1% 7.3% 30 218 28 3442 1472 0.3% 7.3% 2.8% 0.4% 10.5% 7.9% 35 306 31 4151 1780 1.0% 10.4% 4.1% 1.4% 16.1% 12.5% 15 190 25 1959 1738 0.1% 5.0% 1.0% 1.2% 7.2% 6.3% 20 224 27 2075 1878 0.3% 6.4% 1.1% 2.0% 9.7% 8.6% Silverado Offset 25 213 27 2030 1808 0.2% 6.4% 1.1% 1.6% 9.1% 8.1% 30 211 26 2301 1699 0.2% 5.7% 1.3% 1.0% 8.1% 6.9% 35 200 27 2341 1667 0.2% 6.4% 1.4% 0.9% 8.8% 7.5% 15 180 23 1904 1426 0.1% 3.8% 1.0% 0.4% 5.2% 4.3% 20 174 24 2275 1354 0.1% 4.3% 1.3% 0.3% 6.0% 4.7% Yaris Full 25 181 25 2674 1356 0.1% 5.0% 1.7% 0.3% 7.0% 5.4% 30 192 26 2960 1374 0.2% 5.7% 2.1% 0.3% 8.1% 6.1% 35 207 27 3101 1463 0.2% 6.4% 2.3% 0.4% 9.1% 7.0% 15 129 15 1519 1204 0.0% 1.1% 0.7% 0.2% 1.9% 1.2% 20 160 20 1752 1396 0.1% 2.4% 0.9% 0.3% 3.7% 2.8% Yaris Offset 25 172 24 1908 1535 0.1% 4.3% 1.0% 0.6% 5.9% 5.0% 30 205 26 1943 1781 0.2% 5.7% 1.0% 1.4% 8.2% 7.2% 35 200 27 2054 1652 0.2% 6.4% 1.1% 0.9% 8.5% 7.4% 15 160 21 1902 1318 0.1% 2.8% 1.0% 0.2% 4.1% 3.1% 20 160 23 2173 1278 0.1% 3.8% 1.2% 0.2% 5.2% 4.1% Taurus Full 25 172 25 2575 1348 0.1% 5.0% 1.6% 0.3% 6.9% 5.3% 30 199 27 2898 1428 0.2% 6.4% 2.0% 0.4% 8.8% 7.0% 35 231 28 3303 1530 0.3% 7.3% 2.6% 0.6% 10.5% 8.1% 15 147 21 2206 1501 0.0% 2.8% 1.3% 0.5% 4.6% 3.4% 20 167 23 2296 1531 0.1% 3.8% 1.3% 0.6% 5.7% 4.4% Taurus Offset 25 175 25 2183 1606 0.1% 5.0% 1.2% 0.7% 6.9% 5.8% 30 196 27 2306 1681 0.2% 6.4% 1.4% 1.0% 8.8% 7.5% 35 210 28 2159 1768 0.2% 7.3% 1.2% 1.3% 9.9% 8.7%

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Table C15. Partner Vehicle 50th Percentile Occupant Results in Two-Vehicle Crashes with Taurus_ST Target

TAURUS_ST PARTNER VEHICLE OCCUPANT HIII 50th %ile Dummy Combined Chest Neck Chest Neck Combined Speed Femur HIC15 Risk Femur Injury Risk Crash Configuration HIC15 Deflection Tension Deflection Tension Injury Risk (mph) Max (N) (%) Max (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 21 25 2654 488 0.0% 2.6% 0.9% 0.0% 3.5% 2.6% 20 48 29 3466 759 0.0% 4.2% 1.4% 0.0% 5.5% 4.2% Explorer Full 25 110 29 3986 1177 0.0% 4.2% 1.8% 0.0% 5.9% 4.2% 30 241 29 4916 1413 0.4% 4.2% 2.6% 0.0% 7.1% 4.6% 35 279 30 5165 1512 0.7% 4.7% 2.9% 0.1% 8.1% 5.4% 15 11 16 1543 404 0.0% 0.7% 0.4% 0.0% 1.2% 0.7% 20 29 20 2671 514 0.0% 1.3% 0.9% 0.0% 2.3% 1.3% Explorer Offset 25 60 24 2952 621 0.0% 2.3% 1.1% 0.0% 3.4% 2.3% 30 101 30 3486 1134 0.0% 4.7% 1.4% 0.0% 6.0% 4.7% 35 170 31 4030 1201 0.1% 5.2% 1.8% 0.0% 7.0% 5.3% 15 13 24 409 446 0.0% 2.3% 0.1% 0.0% 2.4% 2.3% 20 43 27 941 537 0.0% 3.3% 0.2% 0.0% 3.5% 3.3% Silverado Full 25 127 29 2127 701 0.0% 4.2% 0.7% 0.0% 4.8% 4.2% 30 193 29 1927 875 0.2% 4.2% 0.6% 0.0% 4.9% 4.3% 35 234 30 3150 1094 0.3% 4.7% 1.2% 0.0% 6.2% 5.0% 15 19 22 114 331 0.0% 1.8% 0.0% 0.0% 1.8% 1.8% 20 58 25 220 452 0.0% 2.6% 0.1% 0.0% 2.6% 2.6% Silverado Offset 25 70 24 149 596 0.0% 2.3% 0.0% 0.0% 2.3% 2.3% 30 144 26 532 838 0.0% 2.9% 0.1% 0.0% 3.1% 3.0% 35 185 27 356 1202 0.1% 3.3% 0.1% 0.0% 3.5% 3.5% 15 74 23 4020 742 0.0% 2.0% 1.8% 0.0% 3.8% 2.0% 20 92 24 3768 768 0.0% 2.3% 1.6% 0.0% 3.9% 2.3% Yaris Full 25 112 25 5458 957 0.0% 2.6% 3.2% 0.0% 5.8% 2.6% 30 187 27 6932 1271 0.1% 3.3% 5.5% 0.0% 8.8% 3.5% 35 383 31 7832 1600 2.1% 5.2% 7.4% 0.1% 14.2% 7.3% 15 27 23 460 639 0.0% 2.0% 0.1% 0.0% 2.1% 2.0% 20 75 27 1011 859 0.0% 3.3% 0.3% 0.0% 3.6% 3.3% Yaris Offset 25 109 28 2404 1227 0.0% 3.7% 0.8% 0.0% 4.5% 3.8% 30 314 29 4804 1231 1.1% 4.2% 2.5% 0.0% 7.6% 5.2% 35 393 28 5956 1332 2.3% 3.7% 3.9% 0.0% 9.6% 6.0% 15 34 21 1306 682 0.0% 1.5% 0.4% 0.0% 1.9% 1.5% 20 55 23 2021 867 0.0% 2.0% 0.6% 0.0% 2.6% 2.0% Taurus Full 25 121 25 3713 1247 0.0% 2.6% 1.6% 0.0% 4.2% 2.6% 30 147 26 4447 1294 0.0% 2.9% 2.2% 0.0% 5.1% 3.0% 35 171 26 4893 1333 0.1% 2.9% 2.6% 0.0% 5.6% 3.1% 15 13 17 265 463 0.0% 0.9% 0.1% 0.0% 0.9% 0.9% 20 32 21 515 778 0.0% 1.5% 0.1% 0.0% 1.7% 1.5% Taurus Offset 25 64 23 836 902 0.0% 2.0% 0.2% 0.0% 2.2% 2.0% 30 116 26 2170 1205 0.0% 2.9% 0.7% 0.0% 3.6% 3.0% 35 203 27 3204 1594 0.2% 3.3% 1.2% 0.1% 4.8% 3.6%

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Table C16. Partner Vehicle 5th Percentile Occupant Results in Two-Vehicle Crashes with Taurus_ST Target

TAURUS_ST PARTNER VEHICLE OCCUPANT HIII 5th %ile Dummy Combined Chest Neck Chest Neck Combined Crash Speed Femur HIC15 Risk Femur Injury Risk HIC15 Deflection Tension Deflection Tension Injury Risk Configuration (mph) Max (N) (%) Max (%) II (No (mm) (N) (%) (T)(%) (%) Femur) (%) 15 69 22 1465 1080 0.0% 3.3% 0.7% 0.1% 4.0% 3.4% 20 74 23 1605 1175 0.0% 3.8% 0.8% 0.1% 4.7% 3.9% Explorer Full 25 106 26 1894 1394 0.0% 5.7% 1.0% 0.3% 6.9% 6.0% 30 146 27 1875 1367 0.0% 6.4% 1.0% 0.3% 7.7% 6.8% 35 176 28 1780 1296 0.1% 7.3% 0.9% 0.2% 8.4% 7.6% 15 93 19 1865 940 0.0% 2.1% 1.0% 0.1% 3.1% 2.1% 20 83 20 2118 1056 0.0% 2.4% 1.2% 0.1% 3.7% 2.5% Explorer Offset 25 63 19 2545 1005 0.0% 2.1% 1.6% 0.1% 3.7% 2.2% 30 72 22 2794 1062 0.0% 3.3% 1.9% 0.1% 5.2% 3.4% 35 148 26 2885 1179 0.0% 5.7% 2.0% 0.1% 7.7% 5.9% 15 62 25 511 869 0.0% 5.0% 0.2% 0.0% 5.2% 5.0% 20 78 28 863 963 0.0% 7.3% 0.3% 0.1% 7.7% 7.4% Silverado Full 25 107 28 1042 972 0.0% 7.3% 0.4% 0.1% 7.8% 7.4% 30 97 27 781 922 0.0% 6.4% 0.3% 0.1% 6.8% 6.5% 35 184 29 1038 1010 0.1% 8.2% 0.4% 0.1% 8.8% 8.4% 15 61 24 275 790 0.0% 4.3% 0.1% 0.0% 4.5% 4.4% 20 53 25 506 906 0.0% 5.0% 0.2% 0.1% 5.2% 5.0% Silverado Offset 25 57 25 603 870 0.0% 5.0% 0.2% 0.0% 5.2% 5.0% 30 60 25 636 932 0.0% 5.0% 0.2% 0.1% 5.3% 5.0% 35 60 27 866 926 0.0% 6.4% 0.3% 0.1% 6.8% 6.5% 15 260 27 1804 1074 0.5% 6.4% 0.9% 0.1% 7.9% 7.0% 20 281 28 1786 972 0.7% 7.3% 0.9% 0.1% 8.9% 8.0% Yaris Full 25 360 30 2299 1074 1.7% 9.2% 1.3% 0.1% 12.1% 10.9% 30 340 31 2289 1029 1.4% 10.4% 1.3% 0.1% 12.9% 11.7% 35 337 30 2671 1094 1.4% 9.2% 1.7% 0.1% 12.1% 10.6% 15 158 19 471 769 0.1% 2.1% 0.2% 0.0% 2.3% 2.2% 20 239 25 1107 1048 0.4% 5.0% 0.5% 0.1% 5.9% 5.4% Yaris Offset 25 304 27 1465 1240 1.0% 6.4% 0.7% 0.2% 8.1% 7.5% 30 328 28 2252 1240 1.2% 7.3% 1.3% 0.2% 9.8% 8.6% 35 335 30 2287 1212 1.3% 9.2% 1.3% 0.2% 11.8% 10.6% 15 166 22 1982 1364 0.1% 3.3% 1.1% 0.3% 4.7% 3.6% 20 181 24 1861 1424 0.1% 4.3% 1.0% 0.4% 5.7% 4.8% Taurus Full 25 180 25 2212 1402 0.1% 5.0% 1.3% 0.3% 6.6% 5.4% 30 196 25 2332 1456 0.2% 5.0% 1.4% 0.4% 6.8% 5.5% 35 183 25 2511 1405 0.1% 5.0% 1.6% 0.3% 6.9% 5.4% 15 137 20 2152 1426 0.0% 2.4% 1.2% 0.4% 4.0% 2.8% 20 160 22 2121 1501 0.1% 3.3% 1.2% 0.5% 5.0% 3.8% Taurus Offset 25 160 23 2286 1477 0.1% 3.8% 1.3% 0.5% 5.6% 4.3% 30 176 25 2134 1505 0.1% 5.0% 1.2% 0.5% 6.7% 5.5% 35 191 28 2118 1511 0.1% 7.3% 1.2% 0.5% 9.0% 7.9%

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