Scaling Measures and Improvement of Data Collection

Quality Criteria for the Safety Assessment of Cars Based on Real-World Crashes

Report of Sub-Task 1.3/4.3

CEA/EC SARAC II

QUALITY CRITERIA FOR THE SAFETY ASSESSME OF CARS BASED ON REAL-WORLD CRASHES

Funded by the European Commission,

Directorate General TREN

SARAC II

Quality Criteria for the Safety Assessment of Cars based on Real-World Crashes

Project Number: SUB/B 27020B-E3.S0717321-2002

REPORT of Sub-task 1.3

Scaling Measures and Improvement of Data Collection

Brian Fildes, Liam Fechner, and Astrid Linder

Monash University Accident Research Centre Melbourne, Australia

November 2005

CEA/EC SARAC II

QUALITY CRITERIA FOR THE SAFETY ASSESSME OF CARS BASED ON REAL-WORLD CRASHES

Funded by the European Commission,

Directorate General TREN

International Project Management Comité Européen des Assurances (CEA) Prof. Dr. Klaus Langwieder SARAC Members European Commission (EC) Comité Européen des Assurances (CEA) DG TREN 26 Boulevard Haussmann 28 Rue Demot FR-75009 Paris B-1040 Brussels Monash University Helsinki University of Technology Accident Research Centre (MUARC) Laboratory of Transportation Engineering Building 70, P.O. Box 2100 Clayton, 3800 Victoria, Australia FIN-02015 HUT, Finland BMW Group Bundesanstalt für Straßenwesen Centro Zaragoza Vehicle Safety (BASt) Instituto de Investigación Sobre D-80788 München Brüderstraße 53 Reparación de Vehiculos, S.A. D-51427 Bergisch Gladbach Carretera Nacional 232, km 273 E-50690 Pedrola (Zaragoza) DaimlerChrysler AG Department for Transport FIA Foundation for the Automobile Zone 1/29a Great Minister House and Society D-71059 Sindelfingen 76 Marsham Street 8 Place de la Concorde London, SW1P 4DR United Kingdom Paris 75008 France

Ministry of Transport and Finnish Motor Insurers’ Centre FOLKSAM Insurance Group Communications of Finland (VALT) Research/Traffic Safety P.O. Box 31 Bulevardi 28, S-106 60 Stockholm FIN 0023 Helsinki FIN-00120 Helsinki

Ford Motor Company German Insurance Association (GDV) Honda Motor Europe Safety Data Analysis (SDA) German Insurance Institute for Traffic Wijngaardveld 1 Automotive Safety Office (ASO) Engineering 9300 Aalst Belgium Köln-Merkenich / Spessartstraße Friedrichstrasse 191, D-10117 Berlin D-50725 Köln Insurance Institute for Highway ITARDA IVT Heilbronn Safety (IIHS) & Institute for Traffic Accident Research Institut für Verkehrs- und Highway Loss Data Institute (HLDI) and Data Analysis Tourismusforschung e. V. 1005 N. Glebe Road Kojimachi Tokyu Bldg. 6-6 Kojimachi, Kreuzäckerstr. 15 Arlington, VA 22201 USA Chiyoda-ku Tokyo 102-0083 Japan D-74081 Heilbronn Japanese Automobile Research Laboratory of Accidentology, Loughborough University Institute (JARI) Biomechanics and Human Behaviour Vehicle Safety Research Centre 2530 Karima, Tsukuba PSA Peugeot-Citroën/RENAULT Holywell Building Loughborough Ibaraki 305-0822, Japan (LAB) Leicestershire LE 11 3 UZ UK 132 Rue des Suisses 92000 Nanterre (France) National Organisation for Automotive Swedish Road Administration (SRA) Technische Universität Safety and Victims Aid (NASVA) Röda Vägen Braunschweig 6-1-25, Kojimachi Chiyoda-Ku, S-78187 Borlange Institut für Mathematische Stochastik Tokyo, 102-0083, Japan Pockelsstr. 14 D-38106 Braunschweig Verband der Automobilindustie (VDA) Volkswagen AG Westendstr. 61 1777 Unfallforschung D-60325 Frankfurt/Main D-38436 Wolfsburg

CEA/EC SARAC II

QUALITY CRITERIA FOR THE SAFETY ASSESSME OF CARS BASED ON REAL-WORLD CRASHES

Funded by the European Commission,

Directorate General TREN

Document Retrieval Information Report No. Date Pages SARAC_2_215 November 2005 43

Title and Subtitle Scaling Measures and Improvement of Data Collection

Author(s) Brian Fildes, Liam Fechner, and Astrid Linder

Performing Organisation Monash University Accident Research Centre Building 70, Monash University, Clayton, Victoria, 3800 Australia

Sub-Task Participants Pilot Brian Fildes Monash University Accident Research Centre Sub Contractors Timo Ernvall Helsinki University of Technology Jens-Peter Kreiß TU Braunschweig Heinz Hautzinger IVT Ana Olona Centro Zaragoza (CZ) Advisors Robert Zobel Volkswagen Claus-Henry Pastor BASt Klaus Schmelzer BMW AG Falk Zeidler DaimlerChrysler Paul Fay Ford Anders Kullgren Folksam Insurance Thomas Hummel GDV Matthew Bollington Dept. Transport, UK Yves Page LAB, France Observers Minoru Sakurai & OSA/JARI Japan Kazunori Mashita Abstract

This research project set out to examine a number of aspects related to scaling measures and improvement of data collection for specifying quality criteria for the safety assessment of cars based on real-world crashes. Issues related to Event Data Recorders (EDRs), the availability of in-depth databases, the identification of a limited range of popular cars in Europe, and alternative measures of safety were discussed and a number of important findings eminated from this research for the future of rating vehicle crashworthiness and aggressivity. It was recommended that in any future research into the Safety Rating of passenger vehicles by SARAC, that resources be made available to trial the use of alternative measures of safety and in-depth data more fully. Keywords: VEHICLE IDENTIFICATION NUMBER, SAFETY, RESEARCH, CRASH ANALYSIS

The views expressed are those of the authors and do not necessarily represent those of CEA, GDV or any of the participants of the SARAC committee.

CEA/EC SARAC II

QUALITY CRITERIA FOR THE SAFETY ASSESSME OF CARS BASED ON REAL-WORLD CRASHES

Funded by the European Commission,

Directorate General TREN

Acknowledgements

The authors would like to thank Professor Hampton Clay Gabler, Virginia Tech (formerly Rowan University), in particular, for his valuable material and input in preparing this report.

We are especially grateful to the assistance provided by members of the SARAC committee for their valuable contributions in the provision of additional materials and review comments during the preparation of this report.

CEA/EC SARAC II Table of Contents

Table of Contents

EXECUTIVE SUMMARY ...... 1 1 INTRODUCTION ...... 3 2 EVENT DATA RECORDERS...... 5 2.1 IMPACT SEVERITY ...... 6 2.2 EVENT DATA RECORDER MODELS ...... 6 2.2.1 Specifications and Standards ...... 6 2.3 ORIGINAL EQUIPMENT MANUFACTURERS ...... 8 2.3.1 Delphi...... 11 2.4 AFTER-MARKET EVENT DATA RECORDERS ...... 11 2.4.1 Independent Witness, Inc...... 11 2.4.2 Siemens-VDO...... 12 2.4.3 MacBox...... 12 2.4.4 DriveCam...... 12 2.5 RESEARCHER DEVELOPED EVENT DATA RECORDERS ...... 13 2.5.1 Crash Pulse Recorder ...... 13 2.5.2 Accident and Near Miss Drive Recorders...... 14 2.5.3 Path Reconstruction ...... 15 2.6 AUTOMATIC COLLISION NOTIFICATION...... 15 2.7 EVENT DATA RECORDER RESEARCH ...... 16 2.7.1 Validation of Event Data Recorders ...... 16 2.7.2 Accident Analysis and Reconstruction ...... 16 2.7.3 Injury Mechanisms and Prediction...... 18 2.7.4 Improving Driver Behaviour and Reducing Collisions ...... 19 2.8 USERS OF EVENT DATA RECORDERS...... 20 2.9 EVENT DATA RECORDER DATABASES ...... 21 2.9.1 National Highway Traffic Safety Administration...... 21 2.9.2 Independent Witness, Inc...... 22 2.9.3 Safety Intelligence Systems...... 22 2.10 LIMITATIONS OF EVENT DATA RECORDERS...... 22 2.11 LEGAL AND PRIVACY ISSUES...... 23 2.12 CONCLUSION ...... 25 3 IN-DEPTH DATABASES ...... 27 3.1 DATA AVAILABLE...... 28 3.1.1 Data Observations ...... 28 3.2 IN-DEPTH SUMMARY ...... 30 4 POPULAR EURONCAP TESTED CARS ...... 31 5 ALTERNATIVE MEASURES OF INJURY OUTCOME ...... 33 5.1 CURRENT MEASURES ...... 33 5.2 ALTERNATIVE MEASURES...... 33 5.2.1 The Use of AIS Codes ...... 34 5.2.2 Functional Capacity Index (FCI) ...... 34 5.2.3 Injury Costs (Harm)...... 35 5.2.4 International Classification of Disease...... 35 5.2.5 Implementation of these Measures ...... 35 5.2.6 The Next Step...... 36

CEA/EC SARAC II Table of Contents

6 CONCLUSIONS AND RECOMMENDATIONS...... 38 6.1 RECOMMENDATIONS ...... 38 7 REFERENCES ...... 39

CEA/EC SARAC II Executive Summary

Executive Summary

Event Data Recorders (EDRs)

EDRs were shown to have potential to provide useful detailed crash information not available by conventional crash reconstruction techniques. This technology is the most reliable method of measuring impact severity from the crash pulse and can also provide real time information on driver inputs such as steering and brake use. In addition, EDRs have been shown to be effective in reducing collisions and correcting driver behaviour in fleets.

There is no regulation currently on what data elements and format that EDRs should record. Manufacturers of this equipment do appreciate that EDRs can provide information critical for crash analysis thus enable correlating impact severity with injury potential.

Furthermore, data from these systems can assist with the identification of crash occurrence from the use of Automatic Collision Notification systems and facilitate appropriate triage procedures and medical response in the event of a crash.

Cheaper memory and more extensive sensors will allow future EDRs to record substantial important crash information vital for understanding crashes and their causes more fully. This has the capability of significant improvement in safety generally in the years ahead. Appropriate industry regulation and legal stability is critical to ensure these data are available and not misused, although the public perception of the benefits of EDRs must be addressed before their use will become widely accepted.

Availability of In-Depth Databases

A number of government, industry and private sources currently collect in-depth data around the world and a selection of these is described in some detail in Chapter 3. Many of these databases are commercial and not currently public availability. While it may be possible to negotiate for cases with database owners, their use for retrospective assessment of make and model crashworthiness and aggressivity may present difficulties for some owners.

A number of issues were identified in analysing the details provided by the database owners. These include the number and age of cases, the type of inspection undertaken, entrance criteria for inclusion of cases, and impact severity and injury assessment. Of particular note, though, there was a reasonably high degree of consistency across these data in terms of range and level of detail collected.

Popular EuroNCAP Tested Cars

CEA/EC SARAC II Executive Summary

A number of particular makes and models of popular European cars identified from records of EuroNCAP tested vehicles for which there were reasonable numbers of in-depth cases available in German, UK and European databases. This exercise was undertaken to feed into later research into the feasibility of combining in-depth cases for comparing with prospective crash test results.

Alternative Measures of Safety

The final section of this report addresses an earlier call for alternative measures of injury outcome to those currently used in existing retrospective rating systems. A number of these measures were discussed in terms of their strengths and limitations and the implementation of these in suitable databases was also explored. While it is recognised that many of them have the potential to provide a more detailed assessment of injury outcome for use in rating safety, they are of somewhat limited use without the inclusion of relevant additional details in existing mass databases.

It was recommended that in any future research into the Safety Rating of passenger vehicles by SARAC, that resources be made available to trial the use of alternative measures of safety and in- depth data more fully.

Recommendations

A number of recommendations emanating from the research conducted in this report are listed in Chapter 6 of the report.

2 CEA/EC SARAC II Introduction

1 Introduction

A high quality rating system depends on having high quality data available on which the various safety ratings are based. Current databases suitable for conducting statistical analyses are limited for widespread use. Moreover, they have minimal details on injuries sustained by occupants in crashes. The need for more information on scaling measures and improved data collection was identified in the previous SARAC 1 research program. Subtask 1.3 was designed to address this need where a number of activities were undertaken including:

• A literature review of Event Data Recorders (EDRs);

• Determination of in-depth databases available around the world able to address some of these issues;

• The identification of a limited number of popular European cars for which there is sufficient data available to initiate a case analysis for comparison with the statistical findings; and

• An examination of alternative measures of safety (injury scaling, harm, social cost).

Event Data Recorders (EDRs) offer considerable benefit to crash databases for providing these details for inclusion in mass databases. While these devices are available and in some instances, versions are installed in today’s vehicles, they are rarely used for road safety purposes, even though they have good potential. A review of many of the issues surrounding these units would be useful in helping to identify their true potential and some of the problems and difficulties associated with them. Such a review will be undertaken in Chapter 2 to improve our knowledge and identify steps that could be taken to see them installed in all new motor vehicles for safety improvement. The legal aspects of having these data recorders also needs to be addressed, given the potential for data from these units to be misused.

It was hoped that the increased amount of detail available in these data would be useful for comparing with the ratings made using police and insurance data. In particular, details on impact and injury severity, along with more details on the extent of injury measured using AIS and body region classifications could be potentially useful in addressing some of the possible shortcomings in mass databases. Chapter 3 sets out to identify a number of in- depth databases available around the world for undertaking such a comparison along with the various factors collected, their entrance criteria, and the available of these data for safety research generally.

Subtask 4.3 in this SARAC research program aims to test the feasibility of combining in- depth and mass data and any problems and/or shortcomings associated with this process. In

CEA/EC SARAC II Introduction

Chapter 4, the feasibility of conducting such an analysis will be attempted based on a limited number of makes and models for which there are sufficient cases available to attempt this analysis. A number of popular European cars will be identified for which there are EuroNCAP test results as well as a reasonable number of in-depth crash cases.

Retrospective rating systems are predominantly forced to use measures of injury outcome available in existing databases, typically including measures of injury outcomes, such as whether the occupant was killed, seriously injured (hospitalised), sustained a minor injury or was uninjured. These assessments are often made in the field and thus tend not to be totally reliable and are at best crude measures of injury outcome. The final Chapter of this report is a discussion of alternative measures of injury outcome such as detailed injuries, injury scales (AIS, ISS, etc) and the use of other measures including Harm and social cost. The challenge for using these alternative measures with current mass databases will also be addressed.

4 CEA/EC SARAC II Event Data Recorders

2 Event Data Recorders

An Event Data Recorder (EDR) is a device that is mounted in a vehicle to record data related to a specific event, typically an accident. EDRs were originally incorporated into airbag control systems where mechanical accelerometers were used to determine if an accident was severe enough to require deployment of the airbags. In 1974, General Motors (GM) included data recorders with their airbag systems to record airbag firing information and fault codes within the airbag system in selected vehicles. This technology evolved with the invention of silicon accelerometers to the point where, in 1994, GM’s EDRs were capable of recording data on impact severity. More recent EDRs are capable of recording not only acceleration/deceleration data, but also driver inputs and vehicle status prior to the accident. Real time acceleration data cannot be retrieved by conventional accident reconstruction techniques.

EDRs come in a variety of different forms, depending on the manufacturer and intended use (readers are referred to Gabler et al, 2004, for a comprehensive overview of all aspects of EDRs in the USA). In addition to those integrated into airbag systems there are stand alone after-market EDRs, driver monitors that can detect poor driving, driver-logging systems for truck fleets, accident and near-miss recorders, and Automatic Crash Notification systems. EDRs also have a variety of names such as Accident Data Recorder, Crash Pulse Recorder, and drive- or video drive-recorders and are frequently referred to as ‘black boxes’. In this review ‘EDR’ will be used as a general term representing all of these different names, with more specific names being used for greater clarity as required.

An EDR works by constantly monitoring vehicle acceleration as well as other parameters such as seat belt use, braking, etc. Each of these inputs is sampled at an appropriate rate. For example, acceleration might be sampled at 1000Hz while seat belt use is sampled at only 1Hz. These inputs are stored for a few seconds until being overwritten with new data. This creates a window of data typically about 5 seconds long which is constantly being overwritten. If the acceleration exceeds some trigger level, normally about 2g, as would occur in an accident, the EDR stores all the preceding data permanently. This can then be downloaded and analysed to assist in understanding the accident dynamics.

The exact operation of any particular EDR will differ from this basic model, depending on the manufacturer’s requirements. The recording times vary, video and audio may be recorded, a different trigger level may be used, and there are differences in the inputs recorded.

Modern EDRs are typically about the size of a cigarette packet, although there is some variety in size because of the range of different inputs recorded. They are generally located under the passenger seat in a vehicle where they are protected from collision damage and record approximately the same acceleration pulse as is experienced by the vehicle

CEA/EC SARAC II Event Data Recorders

occupants. An estimated 65-90% of all 2004 model year passenger cars were fitted with some kind of EDR device in the US, with more than half able to record the crash acceleration- or velocity-pulse (NHTSA, 2004).

2.1 Impact Severity

EDRs are primarily built to assist in accident analysis, whether it be monitoring vehicle system performance or driver inputs. When analysing accidents it is important to be able define impact severity. The vehicle resultant change in velocity, delta-V or ∆V, is the principal descriptor of impact severity in most crash databases. Conventionally ∆V is determined from detailed measurements of vehicle deformation, which are then fed into a computer program such as WinSmash or Crash3. These programs return a ∆V value based on the energy of deformation. However, these are only accurate for a limited range of crash types. As the crash deviates from these situations the programs become more inaccurate. ∆V is particularly difficult to measure in sideswipes, collisions with narrow objects, angled side impacts and over/underride. Impact severity measured by ∆V cannot by itself predict the nature and severity of injuries. However ∆V in combination with the Principal Direction of Force (PDOF) can be used to estimate the likely injuries that would be sustained.

In this review ∆V will be used as the measure of impact severity almost exclusively since it is easily recorded by EDRs. It should be noted that ∆V is not necessarily a sufficient indicator of injury potential for crashes where compartment intrusion is a dominant injury factor or when mean acceleration is relatively low. Also, it makes no sense to measure impact severity in terms of ∆V in rollovers or pitchovers (ISO 12353-2, 2003).

2.2 Event Data Recorder Models

2.2.1 Specifications and Standards The development of EDRs has evolved based on the different technical needs of individual manufacturers. Wide variations exist among the data sets recorded by EDRs and techniques used to retrieve the data. Government and standards development organizations have only recently outlined industry standards for data sets and recording formats. However since they were recently introduced and are only voluntary, many installed EDRs do not comply with these standards.

The Society of Automotive Engineers (SAE) released the J1698: Vehicle Event Data Interface-Vehicular Output Data Definition standard in December 2003, pertaining to post- download data format of EDR data. The post-download restriction is to avoid constraints on the type of data recorded, the methods of recording and processing data. 74 output data elements are defined in three groups: high-frequency (data recorded during the crash pulse), low-frequency (pre- and post-crash data), and static (date and time, etc.) (SAE, 2003).

6 CEA/EC SARAC II Event Data Recorders

Although the first edition of J1698 was limited to single frontal impacts, work on side-impact and rollover events is pong (Mash, 2 004). Because vehicle manufactuped create the SAE standard, it is expected to shape the short-term future of EDRs.

The Institute of Elect and Elect ics Engineers (IEEE) released the I standard for Motor Vehicle Event Data Recorde s in September 2004. The standard “defines a protocol for MVEDR output data compatibility and export protocols of MVEDR data elements” (IE It doet prescrib equiata set and recording format but is designed to make EDR data more accessible to end users. Operational requirements such as temperature, humidity, electromagnetic compatibility, capability to withstand an impact, etc. are also included.

In June4, the National Highway Traffic Safety Administration (NHTSA) published it proposal for rulemaking concerning EDRs, including the required data elements that must be recorded by an EDR in the event of an accident. This list also contained elements that must be recorded if equipped, for example passenger airbag deployment status if a passenger airbag is equipped. The required data elements are listed in Ta that since EDRs are not mandatory on new cars there are several EDRs that docord everything on this re element list. Most a fter-market EDRs include user configurable digital inputs that could measure most, if not all, of these data elements. NHTSA intends to make thificatioy for vol untarily installehin the US by September 2008.

CEA/EC SARAC II Event Data Recorders

Table 1. NHTSA recommended minimum EDR data element set. (NHTSA, 2004)

Data Element Recording interval/time

Required Elements

Longitudinal acceleration -0.1 to 0.5 seconds Maximum ∆V Computed after event Speed, vehicle indicated -8.0 to 0 seconds Engine RPM -8.0 to 0 seconds Engine throttle, % full -8.0 to 0 seconds Service brake, on/off -8.0 to 0 seconds Ignition cycle, crash -1.0 seconds Ignition cycle, download At time of download Safety belt status, driver -1.0 seconds Frontal airbag warning lamp, on/off -1.0 seconds Frontal airbag deployment level, driver Event Frontal airbag deployment level, front passenger Event Frontal airbag deployment, time to deploy, driver Event Frontal airbag deployment, time to deploy, FP Event Multi-event, number of events Event Time from event 1 to 2 As needed Time from event 1 to 3 As needed Complete file recorded, yes/no Following other data Elements Required If Equipped Lateral acceleration -0.1 to 0.5 seconds Normal acceleration -0.1 to 0.5 seconds Vehicle roll angle -1.0 to 6.0 seconds ABS activity, engaged/non-engaged -8.0 to 0 seconds Stability control status, on/off/engaged -8.0 to 0 seconds Steering input, angle -8.0 to 0 seconds Safety belt status, front passenger -1.0 seconds Frontal airbag suppression switch, front passenger -1.0 seconds Frontal airbag deployment, time to Nth stage, FP Event Frontal airbag deployment, Nth stage disposal, driver Event Frontal airbag deployment, Nth stage disposal, FP Event Side airbag deployment, time to deploy, driver Event Side airbag deployment, time to deploy, front passenger Event Side curtain/tube airbag deployment, time, driver Event Side curtain/tube airbag deployment, time, FP Event Seat position, driver -1.0 seconds Seat position. Passenger -1.0 seconds Occupant size classification, driver -1.0 seconds Occupant size classification, front passenger -1.0 seconds Occupant position classification, driver -1.0 seconds Occupant position classification, front passenger -1.0 seconds

2.3 Original Equipment Manufacturers

Original Equipment Manufacturers (OEMs) use EDR data to monitor the performance of vehicle safety systems. With no established industry standards until recently, there is a wide

8 CEA/EC SARAC II Event Data Recorders

variety in what OEM EDRs record. GM EDRs, sometimes referred to as the Sensing and Diagnostics Module (SDM), typically record the longitudinal acceleration- or velocity-pulse during the crash, airbag status/response, and a limited range of driver inputs. These inputs include brake status, throttle position, engine RPM and seat belt use and are sampled at 1 second intervals for 5 seconds prior to an accident. The acceleration pulse is measured for 150 milliseconds or 300 milliseconds, depending on the model of EDR. Ford EDRs also record a range of driver inputs and safety system data, but may measure the acceleration pulse for just 78 milliseconds (German et al., 2001). Other OEMs have not released details of their EDRs, or even whether they have any installed, however it is expected that most late model vehicles include some form of data recording.

In 1990, GM released their EDR data format to Vetronix Corporation, so that they could build a Crash Data Retrieval (CDR) system. Vetronix developed a unit that could interface with GM’s airbag controlling hardware and access the crash pulse and other data stored in the event of an accident. GM’s EDR records data if an airbag module runs the algorithm to determine whether or not to deploy the airbag. All timing data is referred to the moment the algorithm runs, that being zero seconds, and is saved in both airbag deployment and non- deployment events. Recently Ford contracted Vetronix to write software to enable the CDR to access their EDRs. As a result, both GM and Ford EDRs record very similar data, displayed by the CDR in the same format. The Vetronix CDR is publicly available for US$2,495. A typical data set downloaded by the CDR is provided in Table 2 (Vetronix, 2004). The output of the CDR is given in Fig’s 1 and 2.

CEA/EC SARAC II Event Data Recorders

Table 2. Typical data set recorded by an OEM EDR and downloaded by the Vetronix CDR. (Vetronix, 2004)

Data Element Recording interval/time

Maximum ∆V for non-deployment event Computed after event Speed, vehicle indicated -5.0 to 0 seconds Engine RPM -5.0 to 0 seconds Engine throttle, % full -5.0 to 0 seconds Brake status, on/off -5.0 to 0 seconds Safety belt status, driver -1.0 seconds Passenger airbag enable, on/off -1.0 seconds SIR warning lamp status, on/off -1.0 seconds Ignition cycle, crash -1.0 seconds Ignition cycle, download At time of download Time to airbag deployment Event ∆V vs. time for frontal airbag deployment event Event Time to maximum ∆V Event Time between non-deploy and deploy event Event

Fig 1. Driver inputs recorded on a GM EDR retrieved by the Vetronix (2004) CDR

10 CEA/EC SARAC II Event Data Recorders

Fig 2. Crash velocity pulse recorded on a GM EDR retrieved by Vetronix (2004) CDR 2.3.1 Delphi Delphi produces several safety devices, which incorporate EDR technology. Called the Sensing and Diagnostic Module (SDM), these devices monitor vehicle acceleration and control safety features accordingly. The SDM can activate airbag deployment and belt pre- tensioners and include crash recording facilities and serial communications. Delphi is currently working on an Occupant Position Recognition system, expected to be released in 2007, to determine when deployment of airbags may be unsafe (Delphi, [Accessed 2005]).

2.4 After-Market Event Data Recorders

2.4.1 Independent Witness, Inc. Independent Witness, Inc. (IWI) produces an after-market EDR which measures acceleration in three orthogonal axes at 1800Hz. The accelerometers have a range of ±60g and the unit has the capacity to store up to 105 events. The data can then be analysed using IWI’s reconstruction software to determine the direction of force, impact severity, angular acceleration and the injury potential. The data is also collected in a report and stored on IWI’s Impact severity Injury Potential (ASIP) database with additional information such as driver age, gender, physical condition, vehicle model, seat position etc. The ASIP, later renamed the Global Access Information Network (GAIN), is intended to provide significant statistical evidence to correlate impact severity and conditions to predicted injury potential (Independent Witness Inc., [Accessed 2005]).

CEA/EC SARAC II Event Data Recorders

2.4.2 Siemens-VDO The Siemens-VDO UDS 2.0 is an after-market EDR that records longitudinal and lateral acceleration, speed and up to 10 digital inputs. When an event occurs the UDS 2.0 records 30 seconds prior and 15 seconds after the event with memory for up to 12 events. The driver can manually initiate recording of an important event. The UDS 2.0 includes automatic collision notification. Siemens-VDO provides software programs for evaluation of event data (VDO, [Accessed 2005]).

2.4.3 MacBox Safety Intelligence Systems, together with Georgia University and Altius Solutions, LLC, developed the MacBox specifically for intense research and driver behaviour analysis. The MacBox captures critical vehicle data such as crash forces, seat belt and brake use, etc. during emergency events and transmits these to a secure crash data vault accessible only to authorised users. The MacBox features five accelerometers, 32MB Flash memory, low power consumption, GPS tracking and wireless digital communications technology. It has the ability to automatically notify emergency medical services of the crash location and the likelihood of severe injury (Altius Solutions, LLC., 2002).

2.4.4 DriveCam DriveCam uses video recording to determine what occurs in an accident, or near accident, situation from a driver’s point of view. A small camera, microphone and accelerometers (longitudinal and lateral) record what the driver sees and hears. In the event of a severe acceleration – typical of an accident or hard braking – the recorder stores 10 seconds of images and sounds before and after the event, creating a 20 second event that shows what happened and also records some pre-crash variables that may help to determine why the event happened. This data can then be downloaded directly to a computer hard drive for storage and playback. A second camera can be included to record the behaviour of the driver and additional memory can be installed. DriveCam is marketed mainly as a means to detect and correct bad driving behaviours in large company vehicle fleets, but has other applications in crash analysis and reconstruction, legal purposes, lowering insurance premiums and improving road safety. However DriveCam does not have sufficiently high sampling rates to provide accurate data on crash pulse shape. DriveCam provides an analysis service to report on events, giving expert opinions on who was at fault in an accident and whether it was preventable. DriveCam has also developed software for analysing and reporting on events, called Hindsight 20/20 (DriveCam, 2004).

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Fig 3. DriveCam view of an out-of-control bus crashing into a car park. Inset: Cabin view of the same event. (DriveCam, 2004)

2.5 Researcher Developed Event Data Recorders

2.5.1 Crash Pulse Recorder The Crash Pulse Recorder (CPR), developed in Sweden by Folksam Insurance, is a simple form of EDR. The CPR is based on a spring mass system, where the movement of the mass is measured in a crash situation. The motion of the mass can be mathematically related to the acceleration pulse of the vehicle in a crash hence an analysis of the recorded mass position with time can be used to determine impact severity or ∆V. If the acceleration reaches some trigger level, approximately 2g, the mass will start to move tripping a micro switch that activates a crystal oscillator circuit. The circuit drives a light-emitting diode at 1000Hz and a photographic film records the position of the mass once for each period of oscillation. The CPR contains its own power supply and records about 120ms of data, depending on the shape of the crash pulse. The CPR is only able to record acceleration along a single axis and does not provide information such as time of accident, brake status etc., but is a very cheap (approx. US$5) and effective means to collect a large amount of data.

CEA/EC SARAC II Event Data Recorders

Figure 4. Mass displacement pulse recorded with the Crash Pulse Recorder (top), and close up of the turning point of the mass (bottom). (Kullgren et al., 1995).

Figure 5. Example of the retrieved acceleration and velocity pulse recorded by the CPR in a laboratory crash test. The dotted lines represent the laboratory accelerometer readings. (Kullgren et al., 1995) 2.5.2 Accident and Near Miss Drive Recorders A Japanese research group (Nishimoto et al, 2001) developed two types of EDR: an accident drive recorder (ADR) and a near miss and accident driver recorder (NADR). The design concept was to create an after-market unit with low cost and diverse data collection. The ADR records only accident data while the NADR records both accident and near miss data. A near miss includes rapid braking, rapid acceleration and rapid steering operations without resulting in an accident. Both drive recorders include longitudinal and lateral accelerometers and yaw sensors. The NADR also measures pitch and roll. The ADR records 55 seconds prior to an accident and 5 seconds after whereas the NADR records 15 seconds of data either side of an accident or near miss. The ADR is triggered at 1.8g and the NADR triggered at about 1.0g. Both units were fitted with a CCD camera to obtain visual data from the driver’s perspective. The advantage of the video recording is enhanced accuracy in accident analysis, particularly if pedestrians or cyclists are involved and possible monitoring of driver behaviour. The cost is about $1000 for the experimentally manufactured unit, with an expected cost of less than $100 for a mass produced model.

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2.5.3 Path Reconstruction Lee and Han (2004) recently developed an EDR and software which, in addition to detecting crashes and deploying airbags, can be used to reconstruct the two-dimensional path a vehicle has taken in the plane of the accident scene, for 50 seconds before and 10 seconds after a crash. The EDR samples at 200Hz one dual-axis (aligned in the longitudinal and lateral axes) accelerometer, with a range of ±50g, for detecting crashes and determining ∆V and the Principal Direction of Force. A yaw rate sensor with a range of ±100 degrees, sampled at 20Hz, is included to enable improved crash analysis. Longitudinal and lateral single-axis accelerometers with a range of ±5g are sampled at 20Hz for the purpose of path reconstruction. The EDR is capable of monitoring vehicle parameters including vehicle speed, engine speed and steering angle and up to nine digital inputs such as brake application, turn signal, gear selection, etc. A 1MB memory chip is able to store up to 32 accident histories. The complementing software takes the raw data from the longitudinal and lateral accelerations and the yaw rate and, with sufficient frequency filtering to remove noise, doubly integrates to obtain the vehicle position in two dimensions with respect to time. Lee and Han report that laboratory tests have confirmed that the EDR can accurately reconstruct a vehicle’s path in normal driving conditions. The EDR has also been tested in a low speed collision and correctly detected the accident without deploying the airbag, as required.

2.6 Automatic Collision Notification

An Automatic Collision Notification (ACN) system includes a version of an EDR, a communication channel, GPS, modem and flash memory. In the event of an accident, ACN systems contact emergency services via the communication channel, usually a mobile phone connection. The ACN transmits to an emergency centre a record of the vehicle location, crash pulse, velocity change and final rest position. The principal direction of force is also indicated. This enables rapid and effective response and the potential to predict accurately the injuries sustained by the occupants (Galganski et al., 2001). Such a system is incorporated into the MacBox EDR.

CEA/EC SARAC II Event Data Recorders

Figure 6. Example of an ACN system. (Bachman and Preziotti, 2001)

2.7 Event Data Recorder Research

2.7.1 Validation of Event Data Recorders In order for EDRs to be used extensively in research the performance limitations must be well understood. Folksam’s Crash Pulse Recorder was tested in a number of full-scale crash tests to evaluate its accuracy and precision (Kullgren et al., 1995). Systematic and random errors were determined in a series of 14 tests with 21 cars involved. Standard laboratory accelerometers were used as a comparison. The random error in ∆V measurements was found to be 2.2km/h. The CPR was found to be suitable for measuring impact severity in large field studies. Over 180,000 CPRs have been installed in vehicles in Sweden and data has been collected from over 700 front and rear impact accidents, providing one of the largest comprehensive accident databases with EDR determined ∆V in the world.

Comeau et al. (2004) tested GM model EDRs and found good agreement with laboratory accelerometers. However the GM EDRs were occasionally unable to collect the entire collision event if the crash pulse duration exceeded the limit of the EDR or if power was lost. It was noted that researchers should take care when using EDR data to analyse collisions, ensuring that they are aware of the limitations of the particular EDR involved.

2.7.2 Accident Analysis and Reconstruction Reliable accident analysis is important for designing effective vehicle and road safety systems, settling insurance claims quickly and correctly, shortening legal processes and providing information on contributing factors to the cause of accidents. EDRs assist accident

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analysis by quickly and easily providing accurate information on crash forces and acceleration as well as driver inputs during a collision.

The estimated ∆V is most often underestimated compared to the measured Delta-V from the crash, as showed among others by Gabler et al. (2004) and Andersson et al. (1997). Gabler et al. (2004) analyzed 65 single event cases in the NASS/CDS database where airbag had deployed. In 77% of the cases the estimated Delta-V (from using WinSmash) was underestimated. On average the estimated Delta-V was 20% lower than that measured from the EDR (Event Data Recorder). In 2 of the 65 cases the estimated Delta-V was over 60 % lower than the measured Delta-V. The differences between estimated and measured Delta-V is likely to be even larger when all impact directions and multiple event are considered. Similar results was found by Andersson et al. (1997) who compared the Delta-V calculated from the recorded longitudinal acceleration by the Volvo Digital Accident Research Recorder (DARR) and the estimated EBS (Equivalent Barrier Speed) based on the remaining deformations. Andersson et al. (1997) found that the EBS was lower than the calculated Delta-V for 7 of 9 cases. In one case the EBS was approximately 20 % higher than the calculated Delta-V and in 3 of the cases EBS was less than 50% of the measured Delta-V.

Gabler et al. (2003) collected accident cases from the NHTSA NASS/CDS database in which EDR data was available. In 110 cases involving GM vehicles, the ∆V value obtained from deformation measurements and WinSmash computer analysis was compared to the value recorded by the GM EDR. They found that estimated ∆V deviate from the ∆V measured by the EDR. They suggest that EDRs could replace post-accident analysis as an estimate of ∆V using real-time measurements. EDRs were also shown to recover unknown ∆Vs in some accident cases, such as sideswipes or impacts with narrow objects.

Acceleration pulses, also known as crash pulses, obtained from real-world accidents involving vehicles fitted with EDRs can be used to analyse and reconstruct the accident in research laboratories. Such a reconstruction may involve a computer simulation or even a full-scale crash test. This reconstruction can then be used to verify or discredit claims made by people involved in the accident. Acceleration pulses also have a large impact on the type and severity of injuries sustained in an accident. By recording the acceleration pulse, researchers are able to identify key injury mechanisms and design safety systems to transfer the injury causing load away from the vehicle occupants.

Andersson et al. (1997) analysed data from the Volvo Digital Accident Research Recorder (DARR). 32 different accident pulses were converted from raw data into different severity measures such as ∆V, mean acceleration, maximum deceleration and the Pulse Index. In this instance the EDR was used to compare the different severity measures and show the reliability of each when correlated to dummy responses.

CEA/EC SARAC II Event Data Recorders

In depth analysis of staged collisions and 10 real world accidents was undertaken by German et al. (2001) in Canada. In a number of crash tests the GM and Ford EDRs were shown to be effective in determining ∆V, provided no power loss occurred in the EDR. Analysis of real world accidents showed that conventional analysis can be quite accurate in many cases when compared to EDR records, but this is not always the case. One particular accident involving an underride produced a ∆V of 22km/h using damage analysis but the EDR recorded a maximum ∆V of 13km/h. EDRs were shown to be extremely useful in identifying specific factors related to the occurrence of a collision and in determining impact severity. However, some limitations of EDRs were observed, particularly in instances of power loss.

Donnelly et al. (2001) analysed another accident in which the EDR data provided conflicting evidence to conventional accident analysis techniques. Initial damage analysis suggested an impact with ∆V of 8 to 16 km/h, consistent with the EDR data, however there was little evidence of the 25.7 km/h ∆V impact reported by the EDR. A sports utility vehicle had impacted with a snow bank after an initial collision with another vehicle. In this instance, the EDR provided evidence of a more severe accident than could be predicted by conventional means. Further studies in which EDRs have been successful in providing reliable information otherwise unobtainable include Chidester et al. (2001), German and Chan (2002), Wood et al. (2003) and McClafferty et al. (2004).

While ∆V has been used as a measure of impact severity for many years, accident researchers are looking at better classifications of impact severity that can be more directly related to injury mechanisms. One such measure is mean acceleration, defined as the change in velocity, ∆V, divided by the duration of the crash pulse. Conventional measurements can not retrieve the duration of the crash pulse after the event, hence EDRs are required to measure this quantity. Krafft et al. (2002) assessed the validity of ∆V, mean and peak acceleration as a means of predicting duration of symptoms of whiplash sustained in rear impacts. They used data from Folksam’s CPR and found mean acceleration was the best candidate, with no conclusive correlation between ∆V and duration of symptoms. Linder et al. (2003) found various shapes and pulse durations could yield very similar ∆Vs for the same vehicle model, even though the injuries sustained may be significantly different.

2.7.3 Injury Mechanisms and Prediction A number of studies have been undertaken to determine the influence of the acceleration pulse, ∆V and the PDOF on injury severity and duration of symptoms. By understanding the acceleration experienced by the vehicle and occupants, researchers can develop systems that protect occupants from severe loading likely to cause serious injury. Ydenius et al. (1999) studied the influence of acceleration pulses on injuries in collisions with roadside objects and car-to-car collisions. They used real world data obtained from the Folksam Crash Pulse Recorder. Although there were too few roadside collisions recorded at that time

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to make any conclusions regarding injuries, their paper demonstrated how useful the CPR could be in collecting information regarding impact severity and vehicle crashworthiness in the real world road environment.

Gabauer and Gabler (2004) assessed EDRs as a means to predict injury based on the existing Acceleration Severity Index (ASI) and flail space models. They found that for longitudinal accelerations the ASI is a good predictor of overall injury. The flail space model was found to be a good predictor if the longitudinal impact velocity is known, however this is a weak predictor of head injuries. The EDRs provided information that was not previously available from real world crashes using conventional accident reconstruction techniques.

Kullgren et al. (2000) studied the influence of acceleration pulses in frontal impacts on the severity of neck injury suffered, while rear impact neck injuries were studied by Krafft et al. (2000). The shape of the crash pulse, particularly the mid and last third and the overall duration, was found to be important. Both studies attempted to find some minimum acceleration threshold that could be tolerated without injury.

Donnelly et al. (2001) reconstructed vehicle occupant’s motion in a computer simulation based on the EDR data from a crash and found that the occupant injuries were consistent with the reconstruction. The same team did further reconstructions of occupant motion using EDR data with fair to excellent predictions of major body region trauma sustained in secondary impacts (Galganski et al., 2001). Their computer simulations yielded occupant responses which could have caused the reported injuries in most cases. Their results suggest that EDRs could be used effectively as a means to predict injury severity based on the acceleration pulse. By incorporating injury prediction into ACN systems, EDRs could be used to notify emergency medical services with the expected injury severity.

2.7.4 Improving Driver Behaviour and Reducing Collisions Some studies show that people who know they are being observed alter their behaviour. EDRs have been shown to reduce collisions when drivers are aware that they are fitted. A total of 443 vehicles in the UK, Netherlands and Belgium were equipped with different types of EDRs in the Safety Assessment Monitoring On Vehicle with Automatic Recording (SAMOVAR) research program in 1995.The aim of the project was

• to assess whether there is any accident reduction potential associated with the use of EDRs and,

• to assess the use of EDRs to improve the effectiveness and accuracy of accident investigation.

It was found that there was a 28.1% reduction in accident rate per vehicle-month in vehicles fitted with EDRs and that the analysis of EDR data produced precise results having greater detail in shorter time than conventional crash investigation.

CEA/EC SARAC II Event Data Recorders

In 1996 Berlin Police installed EDRs in all the radio patrol vehicles of seven precincts. A 20% reduction in collisions was seen when compared to the previous year. Consequently, all radio patrol vehicles are now fitted with EDRs.

Some EDRs used to monitor driving behaviour record more than just accidents, recording average speed, time schedules, rapid acceleration and fuel consumption. These are typically referred to as Journey Data Recorders (JDRs). Another European study used data from JDRs as feedback for fleet drivers reported an estimated 20% reduction in crashes among cars, trucks and coaches. Wouters and Bos (2000) collected data covering 3100 vehicle years with 1836 accidents recorded.

DriveCam is essentially a driving behaviour monitor. In events exceeding the trigger acceleration the driver’s reaction to the situation can be assessed and improvements suggested. When the driver is at fault legally, e.g. failure to stop at a red-light, falling asleep at the wheel, etc., these improvements are fairly straightforward. In other events, such as a collision with another vehicle where that driver is at fault, DriveCam is able to show whether the driver executed appropriate accident prevention measures. In situations where a driver’s reaction was not appropriate, given the available reaction time, counselling and education can be provided for the driver. DriveCam customers claim accident reduction rates of up to 62% when using DriveCam EDRs for driver feedback. (DriveCam, 2004).

Uemaya (2001) conducted a 24-month study of 80 taxi drivers and 30 small truck drivers using a Driving Monitoring Recorder (DMR) in Japan. The DMR constantly records vehicle speed and monitors acceleration, counting the incidences of ‘emergency behaviour’. Emergency behaviour is classified as hard braking, exceeding 3.75 m/s2, or hard acceleration, exceeding 3.5 m/s2. An Event Eye Camera and a GPS tracking system were also installed. The study attempted to identify driving characteristics that were likely to cause an accident and to improve driving behaviour through education. After observing driving deficiencies, a number of subjects were interviewed about their driving behaviour and asked to improve. The recorded data was used to compare their driving record with other drivers. After interviews, several improvements were seen in driver behaviour.

2.8 Users of Event Data Recorders

The data obtained from an EDR is useful to many different groups. The following table is a summary of the NHTSA EDR Working Group’s findings.

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Table 3. Potential users of EDR data. (NTHSA, 2001)

Party Use of EDR Data

Manufacturers To improve the design of motor vehicles and diagnose vehicle systems and for legal cover Governments To reduce road fatalities, injuries and property damage To revise vehicle safety performance standards To improve roadside safety, eg. Guard-rail, crash cushions etc. To examine trends in crash location data To manage and deploy appropriate response teams

Police To provide impartial accident information

Insurance Co. For settling claims quickly and accurately

Courts and Lawyers To assist legal proceedings with objective data

Human Factors Research To determine and understand driver involvement in accident situations

Community Groups Trends in crashes for awareness and educational campaign purposes

Fleets and Drivers To monitor and address driver behaviour To educate drivers about vehicle technology For auto-downloading of driver logs To demonstrate proper vehicle operation during a collision

EMS To improve field triage decisions

Vehicle Owner To determine if the vehicle has been in a prior accident and the severity of the accident. Accident Researchers and For the conduct of research related to vehicle performance and Academics subsequent occupant injury outcomes in accidents

For accident reconstruction

2.9 Event Data Recorder Databases

A number of accident databases exist, mostly for the purposes of research into vehicle and road safety, but also for statistical data useful for insurance companies. Those databases that use EDRs to supplement their data are discussed here.

2.9.1 National Highway Traffic Safety Administration NHTSA has collected EDR records from several hundred crashes as part of the Special Crash Investigations (SCI), National Automotive Sampling System Crashworthiness Data System (NASS CDS) and the Crash Injury Research and Engineering Network (CIREN) activities. The SCI team looks at emerging safety issues, one of which is Advanced Occupant Protection Systems. NASS/CDS is a sample of about 5,000 crashes each year throughout the United States. CIREN conducts approximately 400 investigations at hospitals

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each year. Accident records from these databases are available to download from NHTSA (Gabler et al., 2003; NHTSA 2001).

2.9.2 Independent Witness, Inc. Independent Witness, Inc. manages the Global Access Information Network (GAIN), originally called the Impact severity and Injury Potential (ASIP) database. The database includes the following elements for each accident; impact severity (the ∆V or acceleration magnitude), direction, date and time, vehicle make, model and year, road conditions, occupant's age, gender, occupation, income, physical condition, medical treatment, injury severity, resolution time, pre-existing conditions, prior claims, seating position, and belt use. All data entries are correlated with injuries and treatment from similar accidents, as well as associated settlement and management claim costs. GAIN renders a reliable, objective, probability for injury when compared with other individuals experiencing similar forces.

GAIN is intended to provide statistical evidence to correlate impact conditions with predicted injury potential and thus provide a means to objectively evaluate the validity of soft-tissue injuries. This information allows you to have access to substantive evidence to achieve an empirically based, statistically significant potential for injury directly related to the forces of the subject collision. (Independent Witness Inc., [Accessed 2005])

2.9.3 Safety Intelligence Systems Safety Intelligence Systems introduced wireless communications to the MacBox which transmits accident data directly to the Global Safety Data Vault. This data vault, currently operating in the US, provides a secure and private means to store and manage all vehicular crash data. In November 2003, SIS announced that it was combining with IBM to develop the European Safety Data Vault as part of the European Union’s goal to achieve a 50% reduction in vehicle accident related deaths by 2010. Both data vaults include the necessary privacy filters and security firewalls to ensure only authorised users have access to the crash data. The main purpose of these data vaults is to provide information, which can be used to enhance vehicular safety and reduce the severity of road accidents (IBM, 2003).

2.10 Limitations of Event Data Recorders

Currently installed EDRs generally measure acceleration in only one or two directions. While this gives a good approximation of the acceleration of the vehicle there may be other components to the acceleration not recorded. This can lead to an incorrect interpretation of ∆V or impact severity (McClafferty et al., 2004). In order to provide a complete acceleration pulse, an EDR requires at least three linear accelerometers in three different directions – longitudinal, lateral and vertical – and three angular accelerometers to measure pitch, roll and yaw. A complete acceleration pulse would be useful in rollovers, while one or two directions provides enough information for frontal, side or rear impacts.

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Some models of EDRs do not have sufficient recording times to capture entire collision events. In these instances it can be difficult or impossible to determine ∆V from EDR data. A number of studies report incomplete acceleration- or velocity-time data collected from EDRs (Gabler et al., 2003; McClafferty et al., 2004). It has been recommended that the minimum crash pulse duration recorded be 300 milliseconds at 1millisecond intervals in both the longitudinal and lateral axes (Chidester et al., 2001). Additionally, many accidents involve multiple events, for example, a rear impact may push a vehicle into another vehicle in front. In this example, the direction of force and timing order are important for insurance purposes, as well as determining the most likely injury mechanisms. An EDR must be capable of recording the crash pulse of all events in an accident and the relative timing of these.

EDRs must be able to survive severe accidents. Occasionally a total power loss is suffered by the EDR. This can cause incomplete acceleration pulses and inaccurate recording of other accident parameters such as seat belt use (German et al., 2001; German and Chan, 2002). This can be corrected by including an independent backup power source for the EDR, as most after-market devices incorporate.

A typical crash pulse is a combination of a relatively low-frequency signal and a high- frequency signal. Structural vibrations of the vehicle produce high-frequency noise in the crash pulse. Low-frequency offset can also cause a drift in the crash pulse. This noise must be compensated for by low- and high-frequency filtering (Kullgren et al., 1995; Lee and Han, 2003). Filtering can lead to a loss of information, therefore EDR crash pulses should be considered as an approximation of the real pulse experienced by the vehicle and occupants.

Although the limitations of EDRs are rapidly being addressed, it is important to note that many earlier model EDRs are currently installed. Future accident data will continue to come from these vehicles and analysts must be aware of the limitations of each. There is a need for more testing and assessment of EDRs to determine systematic and random errors in all crash situations (McClafferty et al., 2004).

2.11 Legal and Privacy Issues

Recently in the US and Canada, a number of court cases have been heard in which EDR data has been submitted as evidence. On the occasions where the EDR data is accepted by the court it provides accurate and objective data. However there are too few cases that utilise EDR data for any stable precedent to be set. A selection of cases involving EDR evidence follows (Harris Technical Services, 2005).

California v. Beeler, San Diego Superior Court, Case No. SCD158974 (2002). The defendant, driving a Ferrari, crossed the painted centre median striking a Saturn and killing the driver. The CDR was used to download the EDR data from the Saturn and the Ferrari module was downloaded by the module manufacturer. The collision data was admitted at

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trial unopposed. At issue was whether the Ferrari was travelling at 75 mile/h or 64 mile/h in a 45 mile/h zone and whether this conduct was grossly negligent. The defendant was convicted of a felony for crossing a barrier and causing the death of another.

Canada R. v. Daley, 2003 NBQB 20, S/CR/7/02 (2002). This was a Dangerous Driving Causing Death charge where evidence from the EDR was accepted. The EDR from a 2000 Pontiac Sunfire gave a speed of 124 km/h in a 60 km/h speed zone. On-scene evidence corroborated the EDR evidence. The charged party was found guilty.

Canada R. v. Gauthier, 2003 QCCQ, Case No. 500-01-013375-016 (2003). An attempt was made by the defence to prevent the introduction of the EDR evidence alleging it violated the Canadian Charter of Rights and Freedoms. This was denied and the defendant was found guilty of Dangerous Driving Causing Death. The EDR in his Pontiac Sunfire indicated a speed of 131 km/h.

Florida v. Walker, 20th Judicial Circuit, Lee County, Case No. 00-002866CF RTC (2003). This was a criminal case with a two vehicle, head-on collision. The defendant was charged with two counts of Vehicular Homicide. At issue was the defendant's speed and in which lane the collision occurred. The EDR provided evidence the defendant was not speeding at the time of the collision. The jury found the defendant not guilty.

A major concern involved with EDR technology is ownership of the EDR data. It is generally felt that the vehicle owner also owns the data. However, while you can own a vehicle, you cannot own the speed that it is travelling at. The issue becomes more clouded when the vehicle is being leased. Additionally, vehicle manufacturers are often the only people who can decode and interpret the data.

In September 2003, California passed the first US EDR law concerning ownership of the EDR data. The law states that no one can access the EDR data without the owner’s permission or a court order. However, this provides no real legal protection for the owner since if a serious accident has occurred it would be relatively easy to obtain a court order. By January 2004, California was the only US state to have addressed the issue of EDR data (Purdy, 2004).

In the wake of the NHTSA proposal for rulemaking, in which voluntarily installed EDRs are required to meet the outlined specifications by September 2008, public awareness of EDRs has dramatically increased. Many Americans fear that EDRs are simply a stepping-stone to permanent, automatic traffic law enforcement. Combining EDR technology with communications provides a direct means of issuing traffic infringements as soon as a stop sign or speed limit is violated. Most drivers feel the invasion of privacy by civil authorities and insurance companies far outweighs the safety benefits obtained from crash data. They want to be able to turn the EDR off at any time. They fear an increase in insurance premiums for drivers who refuse to install EDRs and are unwilling to bear the cost of installation in new

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vehicles. Although no recommendation was proposed to mandate EDR installation in all new cars the public response was overwhelmingly negative (NHTSA, 2004 (comments)).

2.12 Conclusion

EDRs have the potential to provide significant volumes of useful during-crash information can not be retrieved by conventional accident reconstruction techniques. EDRs are a reliable means of measuring crash pulses and can also provide real time information on driver inputs such as steering and brake use. Crash pulses can be used to define better measures of impact severity such as duration of pulse and mean acceleration, which are much better predictors of injury than current measures.

EDRs have been shown to be effective in reducing collisions and correcting driver behaviour. There is currently no regulation on the data elements and format that EDRs must record, however manufacturers are tending toward improving EDRs for faster, accurate accident analysis. To this end, a number of EDR related databases have been developed, correlating impact severity to injury potential. These databases can assist with injury prediction in Automatic Collision Notification systems and allow appropriate medical response.

The quality and quantity of EDR data will continue to improve. Cheaper memory and sensors will allow future EDRs to record the compete linear and angular acceleration pulse. EDR databases will grow with significant benefits to vehicle accident research. In turn, this will lead to appropriate industry regulation and legal stability, however public perception of EDRs must be addressed before their installation will become widely accepted.

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3 In-Depth Databases

As noted in the introduction to this report, retrospective ratings based on real-world data need to rely on the level of information provided in the databases they employ to determine these ratings. The outcome measures adopted for a number of international retrospective rating systems are shown in Table 3.1 below.

Table 3.1: Overview of a sample of rating system data and measures (Langwieder & Fildes, 2001)

Publishing Data Source Rating Measure used in the publication Organisation

Insurance Institute for FARS fatal database Fatalities per 10,000 registered vehicles Highway Safety - USA

Highway Loss Data Insurance records 1. Occupant injury rate per vehicle (injury and cost) Institute - USA 2. Vehicle damage payment per insured vehicle

Folksam Insurance - Insurance records 1. Relative risk of driver injury in 2-car crash Sweden 2. Risk of death or permant disability 3. Combinations of 1 and 2

Dept. Transport – UK Police database Injury and severe injury rate

University of Oulu Insurance records 1. Relative risk of injury in 2-car urban crashes Finland 2. Number of drivers injured in 2-car urban crashes 3. Total driver injury rate per 100 million km.

Monash University Police and no-fault 1. Rate of driver injury in tow-away crashes Accident Research insurance data 2. Rate of driver death or serious injury Centre – Australia 3. Combinations of 1 and 2

University of Cologne Police records Safety index based on degree of injury and cost Germany

For the most part, current rating systems use either data from police crash reports, insurance records or a combination of both. Safety performance by vehicle make and model is based on rate of injury or relative risk of injury for drivers or front seat occupants in car crashes in these systems. Police data typically includes whether the occupant was killed, severely injured, minor injury, or uninjured. Insurance data can include other injury descriptors such as ICD codes for individual (usually severe) injuries and cost but these are rarely used for assessing crash performance in these systems.

A possibly way of supplementing this information is through the use of more detailed in- depth data from various studies conducted around the world by auto manufacturers, governments and private research groups. As these studies are usually quite involved and costly, they lack the depth of cases available for a statistical comparison. Nevertheless, given the wealth of details available in these crash reports, they can provide much more detail on impact and injury severity than police and insurance data generally. The challenge is how best to use these sample data to supplement mass databases and add to the ratings in a meaningful way.

CEA/EC SARAC II In-Depth Databases

3.1 Data Available

The first step in examining the feasibility of using in-depth data to supplement mass data safety ratings is to review what in-depth data is currently collected, the number of cases and entrance criteria, crash details collected, injuries coded and availability of these data for general safety purposes. Table 3.2 was composed from many of the known data collection agencies around the world and we are especially grateful to the owner/managers of these databases for agreeing to supply these details during the course of this tudy.

3.1.1 Data Observations Interestingly, there was considerable consistency across the range of data collected and data collection procedures which augers well for combining these cases. However, there were also critical differences, particularly in terms of their entrance criteria, which has ramifications for their use in a unified database. Issue of access, too, seem to be a potential impediment. A number of observations can be made about these databases.

3.1.1.1 Number of cases and collection years Some of these data collection activities have been in progress for over 30 years, while others are relatively new. Hence, the number of cases available varies from over 20,000 to a mere 200 or so records. Importantly, also, the vehicle age varies considerably and not all these cases would therefore be relevant for today’s crashes.

3.1.1.2 Data Ownership The majority of these databases are commercial and there may be restrictions and conditions on their use, especially for rating safety performance. Negotiations with the database owners will be required and permission sought on their availability before they could be considered for this purpose.

3.1.1.3 Inspection type Of particular interest is the observation that most of them involve a "retrospective“ inspection procedure, that is, do not involve "on-the-spot“ data collection procedures. Some of them, however, involve at-scene inspections and also combinations of both retrospective and on- the-spot techniques. This raises issues about the quality and generalisability of these cases which needs to be addressed in attempting to merge these cases.

3.1.1.4 Entrance criteria Some variability in the selection criteria for these cases is apparent. For some, a tow-away entrance criteria was used in the selection of eligible cases ( a vehicle-criteria); in others, at least one occupant had to be injured sufficiently to require hospital admission (a person- based criteria). Moreover, some involved a combination of these two, ie; the vehicle had to be towed from the crash scene and at least one occupant had to injured to some degree. Care needs to be exercise, therefore, in selecting cases that are assumed to be equivalent.

28 CEA/EC SARAC II In-Depth Databases Table 3.2: In-Depth databases available in Europe and elsewhere and their level of information available

ASIA Europe VW GDV BMW SAAB FORD MERC CCIS (UK) CCIS PENDANT LAB (FRA) VALT (FIN) CZ (SPAIN) ANCIS (AUS) ANCIS GIDAS (MUH) GIDAS ITARDA (Japan) GM-MUARC (AUS) GM-MUARC General Database How many cases are currently in 220 535 3,300 ~20,000 5,100 9,167 13000 1,100 17000 ~6200 3554 1909 17000 700 Characteristics your database? Year of First Case 1999 1993 1993 1983 1998 1999 1970 1992 1968 1970 1969 1975 1987 2003 Year of Most Recent case 2003 2003 2004 2005 2002 2004 2003 2004 2004 2005 2004 2004 2004 2005 Data format Access Access Oracle Access dBase SIR Access SAS Access SAS CAST * Oracle Oracle SIR Access Restrictions of use (e.g. ownership) - yes VW Is you Inspection Type… Retrospective 6) no yes or On-the-Spot - -- - yes no Do you select potential cases Age or Type of Vehicle - 6) yes yes 6) based on… Level of Injury 1) - yes yes Level of Vehicle Damage - 6) yes No Types of collision partners - 6) yes yes 6) Age and Sex of Occupant 2) - yes No Seating Position of Occupant - 6) yes No Population Representativeness - -- No - yes No Other Criteria 5) yes No Special Exclusions (e.g. fatalities or No no single vehicle impacts -- no No Are some cases in your Drivers 6) yes yes databases… Front Seat Passengers 6) yes yes Rear Seat Passengers 6) yes yes 6) Children yes yes Does your database include Impact Severity - 6) yes variables involving… Crash Type (side, frontal, multiple vehicle, etc) 6) yes yes 3) 6) Vehicle Safety Systems 3) yes yes Injury Severity - 6) yes yes Treatment levels (e.g. Hospitalised) 6) yes yes MAIS level 3) 6) yes yes ISS score - -- 6) yes yes medical medical ICD-10, - GCS/PTS -- No injury injury yes Other Injury Measures (specify) No AIS98 description description Restraint Use 3) 6) yes yes Location of crash site 6) - yes yes 6) Type of road 4) yes yes 1) MAIS for driver(s), max. MAIS in the vehicle(s) 2) Age and sex for the driver(s) 3) not complete 4) only "autobahn" (motorway) 5) Cost of claims (usually related to level of injury 6) Valid for most of the cases

29 CEA/EC SARAC II In-Depth Databases

3.1.1.5 Impact characteristics Practically all these data involved an assessment of the impact severity of each case, although the method for assessing this was varied. In some cases, it was estimated from an assessment of the damage in terms of a change in velocity that occurred during the crash (∆V) while others use the Equivalent Energy Speed (EES). In addition, calculation of impact severity can be made from a relatively simple assessment of deformation using programs such as Crash 3, PC Crash or SMASH, to a more detailed assessment of vehicle trajectory leading up to the impact. These variants will result in differences in the extent of impact severity across the databases described here.

3.1.1.6 Injuries and injury severity There was reasonable consistency in the recording of injuries across these databases. For the most part, they all used the Abbreviated Injury Severity (AIS) method, albeit using different AIS versions, and an Injury Severity Score (ISS) and Maximium AIS was computed by most databases. This would provides a valuable adjunct to, and allow a more detailed assessment of, case exposure in the mass databases.

3.2 In-Depth Summary

The analysis of in-depth databases across Asia, Europe and the USA has highlighted a number of important issues in considering the possibilities for merging in-depth data from several existing databases. Of particular interest, is the need to ensure that the data collection methods are roughly to be sure of the legitimacy of combining them into a single database. The possibilities for doing this, however, may well be limited by the availability of these cases, due to their commercial ownership.

30 CEA/EC SARAC II Popular EuroNCAP Tested Cars

4 Popular EuroNCAP Tested Cars

Subtask 4.3 calls for an investigation of the feasibility of constructing a single database of in- depth cases for comparing the injury outcome based on real-world crashes with prospective EuroNCAP test results. In carrying out this task, it is important to bring together several factors in constructing the SARACDAT database, namely:

• Case equivalence with EuroNCAP test models; • Equivalent in-depth cases; and • Cases that are made available to SARAC.

Appendix 1 lists the 2003 Update of EuroNCAP tested vehicles which was used in the selection process for the popular European car list. In-depth case details on a selection of these vehicles was generously provided by the BASt in Germany for GDAS (MUH) cases and by the Vehicle Safety Research Centre at Loughborough University for CCIS and Pendant cases. This information has been used to compile a condensed list of vehicles of interest , as shown in Table 4.1 below.

Table 4.1: Popular car list* based on cases from Germany and UK

Make Model Years GDAS CCIS Pendant Total Cases Cases Cases Cases

Opel Corsa 1996-2002 41 220 28 289 Vw Golf 1998+ 89 88 47 224 Ford Focus 1999+ 20 176 27 223 Opel Vectra 1997+ 46 165 8 219 Opel Astra 1999+ 47 99 25 171 VW Polo 1996-2002 55 85 15 155 Peugeot 206 200-2003 9 106 34 149 Ford Fiesta 2000-2003 32 88 11 131 BMW 3-Series 2000-2001 77 33 13 123 Renault Megane 3 1996-2002 36 58 13 107 VW Passat 1997-2001 37 43 21 101 Honda Civic 1998-2001 12 48 5 65 Mercedes C-Class 2001+ 54 8 0 62 Ford Mondeo 2002+ 35 7 4 46 Volvo SV-40 1997+ 0 27 7 34 Toyota Corolla 1998-2003 3 22 8 33 BMW 5-Series 1998+ 0 19 6 25 Audi A4 2001+ 2 6 8 16 Mercedes E-class 1998+ * Table was assembled in January 2005 for the SARAC project.

CEA/EC SARAC II Popular EuroNCAP Tested Cars

32 Alternative Measures of Injury CEA/EC SARAC II Outcome

5 Alternative Measures of Injury Outcome

The final activity undertaken in this Subtask was to discuss the possibilities of alternative measures of injury outcome for measuring vehicle crashworthiness to those listed earlier in Table 3.1and the strengths and weaknesses associated with these.

5.1 Current Measures

As noted in Table 3.1 (page 35), the measures most commonly used by current retrospective rating systems include rate of injury or relative risk of injury for drivers or front seat occupants in car crashes. Police data typically includes whether the occupant was killed, severely injured, sustained minor injuries or was uninjured. Insurance data can include other injury descriptors such as ICD codes for individual (usually severe) injuries

As noted earlier, current rating systems are generally forced to use either data from police crash reports, insurance records or a combination of both as these are the outcomes currently available in the databases used for the analyses. This is because these data are derived from Police reports (typically whether the occupant was killed, severely injured, minor injured or uninjured) or insurance data (which can include other injury descriptors such as ICD codes for individual (usually severe) injuries and costs).

The suitability of current outcome measures is discussed more fully in the Subtask 1.2 report by Hautzinger et al (2005) and will not be discussed here. However, it is important to note that these measures involve a certain amount of inaccuracies and assumptions. It has been proposed by Zeidler et al (2001) that:

"If the intention is to rate the passive safety of different models, the influence of the most important exposure factors such as type of accident, impact severity, and use of restraint systems must be handled. This is especially important for direct measures of injury risk. It is important to normalise for confounding factors in order to put all car models on a comparative basis. But even if this normalisation could be achieved, it should clearly be stated what the rating system is intended to measure, especially considering the injury outcome criterion used, because the definition of the injury outcome itself may extremely influence the results”

5.2 Alternative Measures

A number of alternative measures of injury outcome were proposed by Zeidler et al (2001) in their review, namely: • AIS or MAIS injury score • ICSL (Injury Cost Scale with fatalities • ISS (Injury Severity Score) • Harm Scale • FCI (Functional Capacity Index) • PTS (Poly Trauma Schlüssel) • IIS (Injury Impairment Scale) • GCS (Glascow Coma Scale)

Alternative Measures of Injury CEA/EC SARAC II Outcome

• ICS (Injury Cost Scale / without fatalities) • ICD (International Classification Disease) Not all these outcome measures are routinely collected or likely to be available on any crash injury database. Glasgow Coma Scale measures, for example are used almost exclusively by medical service personnel in deciding on triage or treatment of injured patients and not available for inclusion on databases. However, one or two of the other measures are either available or could be available on some crash databases (eg; in-depth and insurance data) and therefore worthy of considering here.

5.2.1 The Use of AIS Codes The threshold for injury is an issue that causes some variance in the data sets used to assess vehicle crashworthiness. There are large differences between a minor cut or bruise (an AIS 1 severity level) to a limb detachment (AIS 5 or 6) and clearly these differences are important in the final ratings. In terms of safety for the consumer, severe injuries are likely to be an important aspect. While it is recognised that frequent minor injuries such as whiplash are also important issues for insurance companies in terms of payout contribution, this does not seem to be a reasonable outcome measure alone for a quality safety rating system.

It is not unusual for police databases to use the policeman’s judgement about whether the occupant required some form of medical treatment or not. In other words, minor cuts and abrasions would tend to be overlooked. The inclusion of the probability of severe injury would seem to be a useful additional criterion, although preferably as a component of the final score, rather than as a separate or supplementary measure. Better accounts of injury in terms of Abbreviated Injury Severity (AIS) and the ability to capture multiple injuries too, would allow for more precision in the rating estimates. The need to include disability as an additional measure of severity is also relevant here.

5.2.2 Functional Capacity Index (FCI) The Abbreviated Injury Scale (AIS) is a threat to life scale, based on expert judgements by a panel of medical specialists. It is certainly a useful measure of the severity of injury but does not take into account the likely long-term consequences of trauma. In sustaining an injury, many people also loose the ability to perform functions they previously could do. The Functional Capacity Index (FCI) was developed by Ellen MacKenzie and her colleagues during the 1990s to assess what functions individuals have lost as the result of trauma (MacKenzie, et al, 1996). It provides an additional measure that needs to be taken into account when assessing injury consequences. Unfortunately, though, the FCI is not routinely assessed in databases generally so its use in further defining the crashworthiness of passenger cars still seems a long way off.

34 Alternative Measures of Injury CEA/EC SARAC II Outcome

5.2.3 Injury Costs (Harm) Another interesting approach was that adopted by the University of Cologne who published ratings based on their safety index, comprising light and severely injured frequencies costed using average societal costs for these injuries (DM23k and DM190k respectively). The inclusion of costs provides an exciting means of encapsulating relative severity and disability in these assessments, which seems fundamental to what rating systems are attempting to provide. However, its use does require substantially more cases than that involved in the Cologne analysis to avoid bias from inclusion of a small number of unrepresentative severe outcomes.

While not directly raised in the course of these discussions, the use of Harm as a measure of crashworthiness performance has received some attention lately in another system published by Fildes et al (1996). The use of societal Harm provides a useful combination measure of the threat to life (AIS) with a measure of disability and offers a potentially useful overall measure of injury outcome for assessing vehicle safety performance. However, it is a derived variable and its practicality for use on mass databases is questionable. Clearly, there is a need for further research effort into the suitability of Harm as a potentially useful outcome measure in future.

The issue of which costs are the most appropriate is also of interest. An insurance company would have a high priority for claim costs to be the measure as crashworthiness, which could then be included as a marketing feature for insurance premiums. However, there was a high degree of consensus that societal costs are more appropriate as the responsibility of the system should be to the total society, not just insurance payouts.

5.2.4 International Classification of Disease Hospitals typically use the International Classification of Disease (ICD) scale when assigning patient health status. The ICD scale also includes a category, which prescribes the cause of injury in relevant cases, the so-called E-codes. While these are not necessarily compatible with other forms of injury coding, they are nevertheless an improvement over the 4 police measures typically coded as an improved measure of outcome and one currently collected by most public hospitals and some insurance companies. Hence, this measure might offer an improvement when assessing crashworthiness.

5.2.5 Implementation of these Measures As noted earlier, the challenge for using any of these alternative measures with current mass databases rests with their inclusion in current databases used for assessing vehicle safety ratings. The likelihood of inclusion of the three additional measures listed above varies across countries and databases.

Alternative Measures of Injury CEA/EC SARAC II Outcome

Abbreviated Injury Scores are gaining popularity for inclusion in databases maintained by hospitals around the world but are generally not included on police reports or files. Likewise, the Functional Capacity Index is a relatively new concept and is used sparingly in studies conducted the hospital and medical fraternity. Many insurance companies routinely collect injury cost data for crashes involving their clients and potentially has the potential for use, either alone or converted to Harm indices, for use in assessing make and model vehicle safety. This does require, though, detailed records of the various exposure measures relevant for this assessment, which are not always available in insurance databases.

The University of Cologne supposedly attempted using injury cost data as a measure in such a system (Langwieder and Fildes, 2000). From earlier accounts, they derived a safety index by make and model based on the degree of injury (slight, severe, or killed) and the average cost of sustaining these injuries. However, as these data were derived from police records and comprised only a small sample of cases per vehicle make and model, it is questionable if it added anything further than using the original police judgement, used by other systems.

5.2.6 The Next Step If these measures are to be seriously considered for a high quality rating system (which it could be argued is important for improving the quality and reliability of retrospective rating systems), then the fundamental issue is how to include the most promising measures in sufficiently large databases for use in assessing vehicle safety.

In-depth databases at this time offer the level of detailed information necessary to use many of these alternative measures, but individually (generally) lack sufficient cases for a reliable statistical assessment for other than the most popular makes and models. The combination of data from several systems may overcome this to some degree but this is fraught with difficulties associated with commercial ownership, varying entrance criteria and possibly inconsistencies in the range and extent of independent variables collected.

The Subtask 4.3 - SARACDAT – sets out to test the feasibility of combining in-depth data from a number of sources to pilot such a procedure. This is an important initiative for testing the relative merits of many of these alternative measures as well as for enabling greater control over extraneous factors.

It is recommended, therefore, that in any future research into the Safety Rating of passenger vehicles by SARAC, that resources be made available to trial the use of alternative measures of safety and in-depth data more fully.

36 Alternative Measures of Injury CEA/EC SARAC II Outcome

Conclusions and CEA/EC SARAC II Recommendations

6 Conclusions and Recommendations

This report has examined a number of features related to scaling measures and improved data collection of relevance to the retrospective assessment of passenger vehicle safety in Europe. It also included an assessment of available in-depth databases of potential use in any future research in Europe and identified 19 popular European vehicle makes and models for which there are EuroNCAP ratings and sizeable numbers of in-depth cases. The report ended with an analysis of alternative measures of injury outcome and potential usefulness in providing a more definitive account of injury outcome than that currently available in police databases in Europe.

6.1 Recommendations

A number of recommendations of potential interest to the European Commission were noted through this report and are listed below.

EDRs have the potential to provide significant volumes of useful during-crash information cannot be retrieved by conventional accident reconstruction techniques.

• The EC should look at ways of encouraging the widespread adoption of this technology in all new vehicles and its inclusion in crash statistics for enhancing safety ratings.

Current listing of injury and impact severity in police databases is only of limited use for assessing vehicle crashworthiness. Other measures are available that would enhance these measures.

• The EU should look at improving the level of injury severity recorded in police and insdurance data in Europe to improve safety ratings.

• The EU should investigate ways of intergrating impact severity measures in police accident crash data.

38 CEA/EC SARAC II References

7 References

Altius Solutions, LLC. (2002). MACBOX Telematic Event Data Recorder, from http://www.altiusllc.com/Details.asp?Offering_Id=1&Offering_Type=P [Accessed 20 January, 2005]. Andersson, U., Koch, M. and Norin, H. The Volvo Digital Accident Research Recorder (DARR): Converting accident DARR-pulses into different impact severity measures. Proceedings of the International Conference on the Biomechanics of Impact, Hannover, September 1997. Arai, Y., Nishimoto, T., Ezaka, Y. and Yoshimoto, K. Accidents and near misses analysis by using video drive-recorders in a fleet test. Proceedings of the 17th International Technical Conference on the Enhanced Safety Vehicles, Amsterdam, June 2001. Bachman, L. R. and Preziotti, G. R. (2001). Automatic Collision Notification (ACN) Field Operational Test (FOT) Evaluation Report. US Department Of Transport, from http://www.itsdocs.fhwa.dot.gov//JPODOCS/REPTS_TE//13830.html [Accessed on 25 January, 2005]. Chidester, A. B., Hinch, J. and Roston, T. A. Real world experience with event data recorders. Proceedings of the 17th International Technical Conference on the Enhanced Safety Vehicles, Amsterdam, June 2001. Comeau, J-L., German, A. and Floyd, D. Comparison of crash pulse data from motor vehicle event data recorders and laboratory instrumentation. Proceedings of the Canadian Multidisciplinary Road Safety Conference XIV, Ottawa, June 2004. Delphi, Safety and Security, from http://www.delphi.com/products/auto/safety/ids/ [Accessed 20 January 2005]. Donnelly, B. R., Galganski, R. A., Lawrence, R. D. and Blatt, A. Crash reconstruction and injury-mechanism analysis using event data recorder technology. 45th Annual Proceedings of the Association for the Advancement of Automotive Medicine, September 2001. DriveCam (2004), Event Data Recorders, Vehicle Recording Systems & Vehicle Monitoring Devices by DriveCam, from http://www.drivecam.com/products-and-services.html [Accessed 20 January 2005]. Fildes B., Fildes, B.N., Dyte, D., Finch, C.F., Cameron, M.H., Le, T., Vulcan, A.P. , Digges, K.H. & Stolinski, R. (1996). Safecar: A new car safety features rating system, Accident Analysis & Prevention, Gabauer, D. J., and Gabler, H. C. A methodology to evaluate the flail space model utilizing event data recorder technology. Proceedings of the Transportation Research Record 83rd Annual Meeting, Washington, DC, January 2004. Gabauer, D. and Gabler, H. C. (2005). Evaluation of the Acceleration Severity Index threshold values utilizing Event Data Recorder technology. Transportation Research Board, National Academy of Sciences (in press). Gabler, H. C., Hampton, C. E. and Hinch, J. (2004). Impact severity: A comparison of Event Data Recorder measurements with accident reconstruction estimates, SAE Paper No. 2004- 01-1194. Gabler, H.C., Hampton, C. and Roston, T. A. Estimating impact severity: can event data recorders replace crash reconstruction? Proceedings of the 18th International Technical Conference on the Enhanced Safety Vehicles, Nagoya, May 2003. Galganski, R. A., Donnelly, B. R., Blatt, A. and Lombardo, L. V. Crash visualization using real-world acceleration data. Proceedings of the 17th International Technical Conference on the Enhanced Safety Vehicles, Amsterdam, June 2001.

CEA/EC SARAC II References

German, A. and Chan, J. Crash data retrieval systems applied to real-world collision reconstruction. Presented at ICRASH 2002 International Crashworthiness Conference, Melbourne, February 2002. German, A., Comeau, J-L., Monk, B., McClafferty, K. J., Tiessen, P. F. and Chan, J. The use of event data recorders in the analysis of real-world crashes. Proceedings of the Canadian Multidisciplinary Road Safety Conference XII, London, Ontario, June 2001. German, A., Monk, B., McClafferty, K. J., Tiessen, P. F. and Chan, J. The use of event data recorders in the analysis of real world crashes. Proceedings of the Canadian Multidisciplinary Road Safety Conference XIV, Ottawa, June 2004. Harper, J. (2004). Thinking out of the black box, in The American Spectator, from http://www.spectator.org/dsp_article.asp?art_id=7492 [Accessed 20 January 2005]. Harris Technical (2005), EDR Case Law, from http://www.harristechnical.com/cdr5.htm [Accessed 20 January 2005]. IBM (2003), Safety Intelligence Systems Teams with IBM to Provide Auto Crash Information in Ireland, [Accessed 20 January 2005 from: http://www.ibm.com/news/nl/2003/11/20031107_nl_nl_autoongelukken_informatie.html]. IEEE (2004). 1616-2004 IEEE Standard for Motor Vehicle Event Data Recorders (MVEDRs), Institute of Electrical and Electronics Engineers, September 2004. Independent Witness Inc., The Witness Asset Protection System, from http://www.iwiwitness.com/witness.php [Accessed 20 January 2005]. ISO 12353-2 (2003), Road vehicles – Traffic accident analysis, Part 2: Guidelines for the use of impact severity measures. Kowalick, T. M. Pros and cons of emerging Event Data Recorders (EDRs) in the highway mode of transport. Vehicular Technology Conference, IEEE VTS 53rd, 2001. Krafft, M., Kullgren, A., Tingvall, C., Bostrom, O. and Fredriksson, R. (2000). How impact severity in rear impacts influences short- and long-term consequences to the neck. Accident Analysis and Prevention, 32, pp. 187-195. Krafft, M., Kullgren, A., Ydenius, A. and Tingvall, C. The correlation between crash pulse characteristics and duration of symptoms to the neck – crash recording in real life rear impacts. Proceedings of the 17th International Technical Conference on the Enhanced Safety Vehicles, Amsterdam, June 2001. Kullgren, A., Krafft, M., Nygren, A. and Tingvall, C. (2000). Neck injuries in frontal impacts: influence of crash pulse characteristics on injury risk. Accident Analysis and Prevention, 32, pp. 197-205. Kullgren, A., Lie, A. and Tingvall, C. (1995). Crash pulse recorder – validation in full-scale crash tests. Accident Analysis and Prevention, 27 (5), pp. 717-727. Linder, A., Avery, M., Krafft, M. and Kullgren, A. Change of velocity and pulse characteristics in rear impacts: real world and vehicle tests data. Proceedings of the 18th International Technical Conference on the Enhanced Safety Vehicles, Nagoya, May 2003. Linder, A., Avery, M., Kullgren, A. and Krafft, M. Real-world rear impacts reconstructed in sled tests. Proceedings of the International Conference on the Biomechanics of Impact, Graz, September 2004. Gabler, H.C., Gabauer, D.J., Newell, H.L. and O’Neill, M.E. (2004). Use of event data recorder (EDR) technology for Highway crash data analysis, Report for the National Cooperative Highway Research Program, Transportation Research Board of the National Academies, Washington DC. Lee, W. and Han, I. (2004). Development and test of a motor vehicle event data recorder. Proceedings of Instrumentation and Mechanical Engineers, 218, Part D: Journal of Automobile Engineering, pp. 977-985.

40 CEA/EC SARAC II References

Marsh, J. C. Standards activity and research opportunities with Event Data Recorders. Proceedings of the Canadian Multidisciplinary Road Safety Conference XIV, Ottawa, June 2004. McClafferty, K. J., Tiessen, P. F. and German, A. Real world comparisons of calculated and EDR-recorded delta-V. Proceedings of the Canadian Multidisciplinary Road Safety Conference XIV, Ottawa, June 2004. MacKenzie, E., Damiano A., Miller T & Lucter S., "The Development of the Functional Capacity Index," Journal of Trauma-Injury Infection & Critical Care, Vol. 41, No. 5, pp. 799- 807, November 1996 Needham, P. Collision prevention: the role of an Accident Data Recorder (ADR). Proceedings of the International Conference on Advanced Driver Assistance Systems, September 2001. NHTSA, Event Data Recorders, Proposal of Rulemaking, National Highway Traffic Safety Administration, June 2004. Docket No.: NHTSA-2004-18029. NHTSA EDR Working Group, Event Data Recorders, Summary of Findings, National Highway Traffic Safety Administration, Final Report, August 2001. Nishimoto, T., Arai, Y., Nishida, H. and Yoshimoto, K. (2001). Development of high performance drive-recorders for measuring accidents and near misses in the real automobile world. JSAE Review, 22, pp. 311-317. Ohta, T. and Nakajima, S. (1994). Development of a driving data recorder. JSAE Review, 15, pp. 235-261. Purdy, S. (2004). Your black box – whose data is it anyway? National Motorists Association Foundation News, 15 (1), p. 6. SAE (2003), J1698 – 200312. Vehicle Event Data Interface – Vehicular Output Data Definition, Society of Automotive Engineers, December 2003. Ueyama, M. Driver characteristic using driving monitoring recorder. Proceedings of the 17th International Technical Conference on the Enhanced Safety Vehicles, Amsterdam, June 2001. Vetronix (2004). Crash Data Retrieval System, from http://www.vetronix.com/diagnostics/cdr/index.html, [Accessed 24 January 2005]. VDO UDS 2.0, product literature from http://www2.vdo.com/vdo/sycomax/files/508131_UDS2.0_gb.pdf [Accessed 20 January 2005]. Wood, D. P., Ydenius, A. and Adamson, D. (2003). Velocity changes, mean accelerations and displacements of some car types in frontal collisions. IJCRASH, 8 (6), pp. 591-603. Woodhouse, M. Motor vehicle event data recorders and personal privacy. Proceedings of the Canadian Multidisciplinary Road Safety Conference XIV, Ottawa, June 2004. Wouters, P. I. J., and Bos, J. M. J. (2000), Traffic accident reduction by monitoring driver behaviour with in-car recorders. Accident Analysis and Prevention, 32, pp. 643-650. Ydenius, A. Influence of crash pulse duration on injury risk in frontal impacts based on real life crashes. Proceedings of the International Conference on the Biomechanics of Impact, Munich, September 2002. Ydenius, A. and Kullgren, A. Pulse shapes and injury risks in collisions with roadside objects: results from real-life impacts with recorded crash pulses. Proceedings of the International Conference on the Biomechanics of Impact, Sitges, September 1999.

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Appendix 1: List of EuroNCAP tested vehicles (2003 Update)

EURO NCAP Tested Vehicles 1 Fiat Punto 55S 1996 35 Toyota Camry 2.2 (LHD) 1998 2 Ford Fiesta 1.25 LX 16V 1996 36 Saab 9-5 2.0 (LHD) 1998 3 Nissan Micra 1.0L 1996 37 Vauxhall Omega 2.0Gl/GLS (LHD) 1998 4 Renault Clio 1.2RL 1996 38 Volvo S70 2.0/2.5 10V (LHD) 1998 5 Rover 100 1996 39 Ford Focus 1.6 (LHD) 1999 6 Vauxhall Corsa 1.2LS 1996 40 Mercedes A140 Classic (LHD) 1999 7 Volkswagen Polo 1.4L 1996 41 Vauxhall Astra 1.6i Envoy 1999 8 Audi A4 1.8 1997 42 Ford Escort 1.6 LX 1989 9 BMW 316i 1997 43 Nissan Almera 1.4GX 1999 10 Citroen Xantia 1.8i Dimension 1997 44 Renault Espace 2.0RTE (LHD) 1998 & 1999 11 Ford Mondeo 1.8LX 1997 45 Toyota Picnic 2.0GS 1999 12 Mercedes C180 Classic 1997 46 Peugeot 806 2.0 (LHD) 1999 13 Nissan Primera 1.6GX 1996 47 Nissan Serena 1.6 (LHD) 1999 14 Peugeot 406 1.8LX 1997 48 Volkswagen Sharan TDI (LHD) 1999 15 Renault Laguna 2.0RT 1997 49 Mitsubishi Space Wagon 2.4 GDI GLX (LHD) 1999 16 Rover 620 Si 1997 50 Vauxhall Sintra 2.2 GLS 1998 17 Saab 900 2.0i 1997 51 Chrysler Voyager 2.5TD (LHD) 1999 18 Vauxhall Vectra 1.8iLS 1997 52 Fiat Punto S60 1.2 (LHD) 1999 19 Volkswagen Passat 1.6L (LHD) 1997 53 Volkswagen Lupo 1.0 (LHD) 1999 20 Audi A3 1.6 1997 54 MCC Smart (LHD) 1999 21 Citroen Xsara 1.4i (LHD) 1998 55 Hyundai Atoz GLS (LHD) 1999 22 Daewoo Lanos 1.4SE (LHD) 1998 56 Vauxhall Corsa 1.0 12v Club 1999 23 Fiat Brava 1.4S 1998 57 Honda Logo (LHD) 1999 24 Honda Civic 1.4i 1998 58 Lancia Ypsilon Elefantino (LHD) 1999 25 Hyundai Accent 1.3GLS (LHD) 1998 59 Honda Accord 1.8iLS 1999 26 Mitsubishi Lancer GLX (LHD) 1997 60 Volkswagen Beetle 2.0 (LHD) 1999 27 Peugeot 306 1.6GLX 1997 61 Saab 9-3 2.0 (LHD) 1999 28 Renault Megane 1.6RT (LHD) 1998 62 Volvo S80 2.4 (LHD) 2000 29 Suzuki Baleno 1.6GLX (LHD) 1998 63 Ford Ka 1.3 (LHD) 2000 30 Toyota Corolla 1.3 Sportif (LHD) 1998 64 Volvo S40 1.8 1997 31 Volkswagen Golf 1.4 (LHD) 1998 65 Toyota Avensis 1.6S 1998 32 Audi A6 2.4 (LHD) 1998 66 Citroen Saxo 1.1 SX (LHD) 2000 33 BMW 520i (LHD) 1998 67 Daewoo Matiz SE+ RHD 1999, 2000 34 Mercedes E200 Classic (LHD) 1998 68 Daihatsu Sirion M100LS (LHD) 2000

42 CEA/EC SARAC II References

69 Fiat Seicento 2000 105 Mercedes M-Class ML270 (LHD) 2002 70 Ford Fiesta 1.25 Zetec 2000 106 Suzuki Grand Vitara 2.7ltr XL-7 (LHD) 2002 71 Nissan Micra L 1.0 (RHD) 2000 107 Chrysler PT Cruiser 2.0ltr (LHD) 2002 72 Peugeot 206 1.3 XR Presence (LHD) 2000 108 Audi A2 1.4 (LHD) 2002 73 Renault Clio 1.2 RTE (LHD) 2000 109 BMW Mini Cooper 1.6 (LHD) 2002 74 Rover 25 1.4i (RHD) 2000/2001 110 Peugeot 607 2.2 Hdi (LHD) 2002 75 Seat Ibiza 1.4 Stella (LHD) 2000 111 Honda S2000 (LHD) 2002 76 Skoda Fabia 1.4 Classic (LHD) 2000 112 Mazda MX-5 1.6 LHD 2002 77 Toyota Yaris 1.0 Terra (LHD) 2000 113 Mercedes-Ben SLK 200 Kompressor (LHD) 2002 78 Volkswagen Polo 1.4 (LHD) 2000 114 Range Rover (RHD) 2002 79 Alfa Romeo 147 1.6 (LHD) 2001 115 Jeep Cherokee2.5 TD Limited (LHD) 2002 80 Honda Civic 1.4 S (LHD) 2001 116 Vauxhall/Opel Frontera 2.2 DTL 16v RHD 2002 81 Nissan Almera Hatch 2001 117 Honda CR-V 2.0 SE (RHD) 2002 82 Peugeot 307 (LHD) 2001 118 Mercedes E-Class 220CDi Elegance LHD 2003 83 Audi A4 2.0 (LHD) 2001 119 Renault Vel Satis 2.2DCi (LHD) 2003 84 BMW 316i (LHD) 2000/2001 120 Citroen C3 SX 1.4 Essence (LHD) 2003 85 Citroen C5 1.8i 16v SX (LHD) 2001 121 Ford Fiesta 1.4 Trend (RHD) 2003 86 Hyundai Elantra 1.6 GLS (LHD) 2001 122 Seat Ibiza Stella 1.2 (LHD) 2003 87 Mercedes-Benz C180 (LHD) 2001 123 Toyota Corrolla 1.4 Terra (RHD) 2003 88 Mitsubishi Carisma 1.8 Comfort (LHD) 2001 124 Saab 9-3 2.0ltr (LHD) 2003 89 Peugeot 406 (LHD) 2001 125 Nissan Primera 1.8 (LHD) 2003 90 Renault Laguna II 1.8 16v (LHD) 2001 126 Subaru Legacy Outback 2.5 (RHD) 2003 91 Rover 75 1.8 (RHD) 2000/2001 127 Hyundai Santa Fe 2.0 GRD (LHD) 2003 92 Skoda Octavia 1.9 Tdi Ambiente (LHD) 2001 128 Land Rover Freelander GS K1.8ltr petrol (RHD) 2003 93 Vauxhall/Opel Vectra 1.8 (LHD) 2001 129 Nissan X-Trail 2.0ltr (LHD) 2003 94 Volkswagon Passat 1.9 Tdi (LHD) 2001 130 Mercedes Vaneo 170 Cdi (LHD) 2003 95 Volvo S60 (LHD) 2001 131 Peugeot 807 2.0 Hdi (LHD) 2003 96 Citroen Picasso 1.6 LX (LHD) 2001 132 Vauxhall/Opel Vectra 1.8 SE (LHD) 2002 97 Fiat Multipla JTD ELX (RHD) 2001 133 Proton Impian 1.6 GX (RHD) 2002 98 Honda Stream 1.7 SE VTEC (RHD) 2001 134 Jaguar X-Type 2.0 (LHD) 2002 99 Mazda Premacy 1.8 Comfort (LHD) 2001 135 Renault Megane II 1.6 16v (LHD) 2003 100 Mitsubishi (Colt) Space Star 1.3 Family (LHD) 2001 136 Vauxhall/Opel Corsa 1.2 Comfort (LHD) 2002 101 Nissan Almera Tino 1.8 Luxury (LHD) 2001 137 Volkswagen Polo 1.2 (LHD) 2002 102 Renault Scenic 1.4 (LHD) 2001 138 Ford Mondeo 1.8 LX (RHD) 2002 103 Vauxhall/Opel Zafira 1.8 (RHD) 2001 104 Peugeot 806 2.0 (LHD) 1999

Australia Monash University Accident Research Centre Belgium Honda Motor Europe France FIA Foundation for the Automobile and Society Laboratory of Accidentology, Biomechanics and Human Behaviour, PSA Peugeot, Citroen, Renault Finland Helsinki University of Technology Ministry of Transport and Communication of Finland Finnish Motor Insurers’ Centre (VALT) Germany Bundesanstalt für Straßenwesen (BASt) German Insurance Association (GDV) Institute for Applied Transport and Tourisme Research (IVT) Technische Universität Braunschweig BMW Group DaimlerChrysler AG Ford Motor Company Volkswagen AG Verband der Automobil Industrie (VDA)