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Alertness Maintaining Tasks: a Fatigue Countermeasure During Vehicle Automation?

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Alertness Maintaining Tasks: a Fatigue Countermeasure During Vehicle Automation? Alertness Maintaining Tasks: A Fatigue Countermeasure During Vehicle Automation? A dissertation submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in the Department of Psychology of the College of Arts and Sciences 2014 by Catherine E. Neubauer M.A. University of Cincinnati, 2011 B.S. University of Central Florida, 2008 Committee Chair: Peter Chiu, Ph.D. ABSTRACT Driver fatigue is a leading cause of vehicular accidents (Lee, 2006). Additionally, development of newer technology such as vehicle automation offers a potential countermeasure to driver fatigue. As vehicle operation becomes increasingly automated, driver fatigue appears to be a pressing safety issue. A number of countermeasures have been evaluated in the attempt to alleviate driver fatigue. In the present context trivia games have been suggested as a fatigue countermeasure but like cell phone use, they may prove distracting. The present study investigated the effects of two especially relevant workload factors on driver performance: automated driving and secondary media usage. Vehicle automation is a relatively new trend among automakers that can potentially alleviate the adverse effects of fatigue and in turn regulate workload, however recent studies have suggested that automation may result in a dangerous state of underload in which effort is withdrawn from the driving task (Desmond, Hancock & Monette, 1998; Funke et al., 2005). A manipulation of full and partial vehicle automation was used to induce fatigue during simulated driving. Participants were also assigned to one of three media device conditions (control, cell phone or trivia). Subjective state response, vehicle control and reaction time to a sudden event were recorded. As predicted, the media devices did help minimize the loss of task engagement and elevated distress produced by vehicle automation. We also extended findings that the media devices helped improve concurrent driver performance, with control driving shown to be associated with the worst vehicle control. However, media usage was not associated with faster response time to subsequent “sudden events”, suggesting that such devices may not enhance alertness during unpredictable events. ii iii ACKNOWLEDGEMENTS I would first like to thank my committee members for their very helpful input on this project. I would especially like to thank my mentors, Gerry Matthews and Peter Chiu, for their guidance and support during this time. I would also like to thank my family who has always supported me throughout my life and also my research assistants, Jessica Bailey, Laqueena Mitchell and Erin Roy for their help with this project. iv TABLE OF CONTENTS TABLE OF CONTENTS………………………………………………………………………..v LIST OF TABLES……………………………………………………………………………..viii LIST OF FIGURES………………...……………………………………………………….......ix Introduction……………………………………………………………………………………....1 Background of Fatigue within the Transportation Setting……………………....………………...4 Cognitive Aspects of Driver Performance……………………….………………………………..7 Attentional Models…………………………...……………………………………………7 Automated Vehicle Systems……………………………………………………………………..10 Workload and Vehicle Automation………………..………………………………….....10 Subjective State and Vehicle Automation………….……….…………………………...12 Secondary Alertness Maintaining Tasks………...……………………………………………….13 The Interaction between Stress, Fatigue and Driving……………………………………………16 Active vs Passive Fatigue……………..…………………………………………………16 The Transactional Model of Driver Stress……………………………………………….17 The Dundee Stress State Questionnaire………………………………………………….18 Personality within the Transportation Context…………………………….…………………….20 The Driver Stress Inventory……………………………………………………………...20 Aims and Objectives………………………...…………………………………………………...22 Method…………………………….…………………………………………………………….24 Participants……………………………………………………………………………………….24 Experimental Design and Simulator Tasks…………….………………………………………...25 v Questionnaires………………………………………………………………………………..…..25 Cell Phone Usage Questionnaire…………………………………………………...……25 The Driver Stress Inventory…………………………………………………………..….26 The Dundee Stress State Questionnaire………………………………………………….26 The Driving Simulator……………..………………………………………………………...…..27 Cellular Telephones and Bluetooth Device……………………………..……………………….28 Driving Tasks, Automation and Secondary Media Conditions……………….…………………29 Practice Drive………………………………………………………………………...…..30 Main Drive…………...…………………………………………………………….…….30 Automation Conditions…………………………………………………………………..30 Secondary Media Conditions…………………………………………………………….31 Performance Assessment………………………...…………………………………..…..32 Procedure……………………………………………………………………………………..….34 Results…………………………….……………………………………………………………..37 Baseline Analyses………………………………………………………………………………..38 Task-induced Effects of Automation and Secondary Media on Subjective Stress State………...38 Perceived Mental Workload………………………….………………………………………….42 Predictors of Subjective State………………………………...………………………………….43 Correlations………………...…………………………………………………………….43 Regression…………..…………………………………………………………..………..44 Driver Performance Measures ……………...………………………………………………...…46 Vehicle control………………………………………………………………………...…46 Response times…………………………………………………………………………...49 vi Crash rates………………………………………………………………………………..51 Discussion………………………………………………………………………………..52 General Discussion……………………………………………………...…..…………………..53 Overview of Findings…………..…………………………….………………………………….53 Theoretical implications…………………………………………………………………………57 Vehicle Automation and Secondary Media………………….…………………………..58 Practical Applications……………………………………...…………………………………….61 The Role of Stress Vulnerability and Individual Differences……………………………………66 Limitations……………………………………………………………………………………….68 Summary and Overall Conclusions……..……………………………………………………….69 REFERENCES………………………………………………………………………………….73 APPENDIX A: Frequency of Cell Phone Use Questionnaire………………………………..82 APPENDIX B: Driver Stress Inventory…………….………………………………………...83 APPENDIX C: Pre-task DSSQ………………………………………………………………...88 APPENDIX D: Post-task DSSQ…………….. ………………………………………………..92 APPENDIX E: Complete List of Trivia Questions………….………………………………..99 APPENDIX F: Script for Cell Phone Conversation……………………...…………………112 APPENDIX G: Informed Consent Form……….……………………………………………115 vii LIST OF TABLES 1. Standardized mean pre and post task scores of the DSSQ for automation and secondary media conditions. 2. Mean overall workload scores and standard deviations for all experimental conditions. 3. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the entire sample. 4. Standard deviation of lateral position for automation and secondary media conditions. 5. Mean response times for automation and secondary media conditions. viii LIST OF FIGURES 1. Experimental setup using a System Technologies, Inc., STISIM Drive, build 2.08.10, a Westinghouse 42-inch LCD monitor and Logitech MOMO racing force feedback wheel. 2. a) Participant phone, LG Rumor 2 b) experimenter phone, LG LX 101 c) JABRA Bluetooth headpiece. Photos for phones and Bluetooth headpiece were obtained via http://cgi.iwirelesshome.com/phones/ and http://www.jabra.com/headsets-and-speakerphones/all- products/bluetooth respectively. 3. Screen shot of the sudden event. 4. Pre to post-drive changes in subjective state for the control, trivia and cell phone conditions. Error bars are standard errors. 5. Standard deviation of lateral position for non-automation and partial automation groups. Error bars are standard errors. 6. Standard deviation of lateral position for the control, cell phone and trivia groups. Error bars are standard errors. 7. Response times for steering and braking between the non-automated, partial and total automation groups. Error bars are standard errors. 8. Response times for steering and braking by control, cell phone and trivia groups. Error bars are standard error ix Introduction The United States is home to the largest transportation sector in the world. Recently, there was an estimated 254.5 million registered passenger vehicles on the road, with a steady increase since 1960 (Department of Transportation, 2007). With the increase in the number of drivers on the road also come numerous traffic accidents. A number of factors can contribute to the cause of an accident, mainly (1) the environment (e.g., roadway, scenery, and weather), (2) the individual (e.g., driver and other road users) and (3) the vehicle (e.g., industrial design) (Shinar, 2007; Evans, 2004). Unfortunately, the individual or “human factor” tends to play the largest role in most accidents. Individual factors can refer to the driver’s emotional or physiological state (i.e., stress or drowsiness) and the driver’s strategies for managing task workload (Saxby et al., 2013). Two such factors gaining much attention in the press are driver fatigue and distraction. Driver fatigue is a highly cited cause of roadway accidents, yet the number of fatigued drivers on the road is still fairly high (Lee, 2006). Estimates of the contribution of fatigue to accidents differ, but one US study estimated that there are 56,000 fatigue-related road crashes annually in the USA, resulting in 1,550 fatalities (NCSDR/NHTSA, 1998). Additionally, The National Highway Traffic and Safety Administration (NHTSA) (2013) estimates that 3,331 people were killed in crashes involving distracted drivers in 2011. Despite the dangers of fatigue and distraction, little is known about how they may interact. In response to recent driving
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