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Driver Behavior Models for Evaluating Automotive Active Safety

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Driver Behavior Models for Evaluating Automotive Active Safety CHALMERS UNIVERSITY OF TECHNOLOGY SE-412 96 Gothenburg, Sweden Telephone: +46 (0)31 772 10 00 www.chalmers.se GUSTAV MARKKULA GUSTAV Driver behavior models for evaluating automotive active safety Driver behavior models for evaluating automotive active safety Driver behavior models for evaluating automotive active safety From neural dynamics to vehicle dynamics GUSTAV MARKKULA 2015 Department of Applied Mechanics CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2015 THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN MACHINE AND VEHICLE SYSTEMS Driver behavior models for evaluating automotive active safety From neural dynamics to vehicle dynamics GUSTAV MARKKULA Department of Applied Mechanics CHALMERS UNIVERSITY OF TECHNOLOGY G¨oteborg, Sweden 2015 Driver behavior models for evaluating automotive active safety From neural dynamics to vehicle dynamics GUSTAV MARKKULA ISBN 978-91-7597-153-7 c GUSTAV MARKKULA, 2015 Doktorsavhandlingar vid Chalmers tekniska h¨ogskola Ny serie nr. 3834 ISSN 0346-718X Department of Applied Mechanics Chalmers University of Technology SE-412 96 G¨oteborg Sweden Telephone: +46 (0)31-772 1000 Chalmers Reproservice G¨oteborg, Sweden 2015 Driver behavior models for evaluating automotive active safety From neural dynamics to vehicle dynamics Thesis for the degree of Doctor of Philosophy in Machine and Vehicle Systems GUSTAV MARKKULA Department of Applied Mechanics Chalmers University of Technology Abstract The main topic of this thesis is how to realistically model driver behavior in computer simulations of safety critical traffic events, an increasingly important tool for evaluating automotive active safety systems. By means of a comprehensive literature review, it was found that current driver models are generally poorly validated on relevant near-crash behavior data. Furthermore, competing models have often not been compared to one another in actual simulation. An applied example, concerning heavy truck electronic stability control (ESC) on low-friction road surfaces (anti-skidding support), is used to illustrate the benefits of simulation-based system evaluation with a driver model, verified to reproduce human behavior. First, a data collection experiment was carried out in a moving-base driving simulator. Then, as a complement to conventional statistical analysis, a number of driver models were fitted to the observed steering behavior, and compared to one another. The best-fitting model was implemented in closed-loop simulation. This approach permitted the conclusion that heavy truck ESC provides a safety benefit in unexpected critical maneuvering, something which has not been previously demonstrated. Furthermore, ESC impact could be analyzed at the level of individual steering behaviors and scenarios, and this impact was found to range from negligible, when the simulated drivers managed well without the system, to large, when they did not. In severe skidding, ESC reduced maximum body slip in the simulations by 73 %, on average. Some specific ideas for improvements to the ESC system were identified as well. As a secondary applied example, an advanced emergency brake system (AEBS) is considered, and a partially novel approach is sketched for its evaluation in what-if resimulation of actual recorded crashes. A number of new insights and hypotheses regarding driver behavior in near-crash situations are presented: When stabilizing a skidding vehicle, drivers were found to employ a rather simple and seemingly suboptimal yaw rate nulling strategy. Collision avoidance steering was found to be best described as an open-loop steering pulse of constant duration, regardless of amplitude. Furthermore, by analysis of data from test tracks as well as real-life crashes and near-crashes, it was found that detection of a collision threat, and also the timing of driver braking or steering in response to it, may be affected by a combination of situation kinematics and processes of neural evidence accumulation. These ideas have been tied together into a modeling framework, describing driving control in general as constructed from intermittent, ballistic control adjustments. These, in turn, are based on overlearned sensorimotor heuristics, which allow near-optimal, vehicle- adapted performance in routine driving, but which may deteriorate into suboptimality in rarely experienced situations such as near-crashes. Keywords: Driver models, control behavior, active safety, system evaluation, simulation i For Charlotte and X, my Papers 0 and VII. iii Acknowledgments The research work presented in this thesis was carried out jointly at the Adaptive Systems group at the Division of Vehicle Engineering and Autonomous Systems, Department of Applied Mechanics, Chalmers University of Technology, and Volvo Group Trucks Technol- ogy, Advanced Technology and Research. Funding for the research came from AB Volvo, VINNOVA FFI (project QUADRA, grant number 2009-02766) and the Transportation Research Board of the National Academies (project SHRP 2 S08(A)). Call me a nerd, but for me, spending five years worth of my life on one, narrow subject has been an absolute privilege. Doing so would not have been possible, nor anywhere near as fruitful, had it not been for a large number of people providing me with their support. Many sincere and heartfelt thanks are therefore due: To friend and former PhD student Dr. Ola Benderius, for taking this journey with me, and staying your wonderful self through both ups and downs. To my main academic supervisor Prof. Mattias Wahde, for your dedication, your insightful advice, and for allowing our work relationship to grow into one of mutual trust and appreciation. Thanks also to my co-supervisors Dr. Krister Wolff and Dr. Hans-Erik Pettersson, and to my co-co-supervisors in the QUADRA Scientific Advisory Board: Prof. Gregor Sch¨oner,Prof. Timothy Gordon, Prof. John D. Lee, Prof. John Wann, and Prof. Heikki Summala. To my project managers at Volvo, especially Dr. Lennart Cider and Peter Wells, for being the nice guys you are and for keeping my PhD work a priority, and likewise to my group managers Joakim Svensson and Helene Niklasson. Thanks also to Dr. Stefan Edlund, for your continuing interest in and support for the QUADRA project. To my friends, colleagues, and favorite amateur neuroscientists Dr. Johan Engstr¨om and Dr. Trent Victor. To the actual neuroscientist, Dr. Sergei Perfiliev. To all of my amiable and talented Volvo colleagues, but especially Johan Lodin, Kristoffer Tagesson, Johan Ekl¨ov,and Niklas Fr¨ojd,for your support in various stages of this work, and likewise to Dr. Leo Laine and Erik Wikenhed. To other colleagues in the QUADRA project, the SHRP 2 analysis project, and elsewhere, providing support not the least in setting up simulator studies and extracting naturalistic data: Dr. Jesper Sandin, Bruno Augusto, Jonas Andersson Hultgren, Christian- Nils Boda, Jonas B¨argman,Dr. Lars Eriksson, Anne Bolling, and Anders Andersson. To my other friends and my family, who have patiently listened to me fussing about my research. This is of course especially true for my wife, now a driver behavior expert despite herself. To you, Veronica. I could not have hoped for a better life companion. To Charlotte: My book is done now, and it looks like I may be getting that hat soon. v Thesis This thesis consists of a set of introductory chapters, and the following papers: G. Markkula, O. Benderius, K. Wolff, and M. Wahde. “Effects of expe- Paper I rience and electronic stability control on low friction collision avoidance in a truck driving simulator". Accident Analysis & Prevention 50 (2013), pp. 1266{1277. O. Benderius, G. Markkula, K. Wolff, and M. Wahde. \Driver behaviour Paper II in unexpected critical events and in repeated exposures - a comparison". European Transport Research Review 6 (2014), pp. 51{60. Excerpt from: T. Victor, J. B¨argman, C.-N. Boda, M. Dozza, J. Engstr¨om, C. Flannagan, J. D. Lee, and G. Markkula. Safer Glances, Driver Inat- Paper III tention, and Crash Risk: An Investigation Using the SHRP 2 Naturalistic Driving Study. Phase II Final Report. Transportation Research Board of the National Academies, 2014. G. Markkula, O. Benderius, K. Wolff, and M. Wahde. \A review of near- Paper IV collision driver behavior models". Human Factors 54.6 (2012), pp. 1117{ 1143. G. Markkula, O. Benderius, and M. Wahde. \Comparing and validating Paper V models of driver steering behaviour in collision avoidance and vehicle stabilization". Vehicle System Dynamics 52.12 (2014), pp. 1658{1680. G. Markkula. \Modeling driver control behavior in both routine and near- Paper VI accident driving". Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Vol. 58. Chicago, IL, Oct. 2014, pp. 879{883. Other, related, publications by the author: [10, 11, 26, 34, 109, 113, 114] Manuscript in preparation, cited in this thesis: [107] vii Technical terms and abbreviations active safety evaluation, 2 inverse TTC (invTTC), 17 active safety systems, 1 purpose of, 3 looming, 17 adaptive behavior, 3 model, 19 Advanced Crash Avoidance Technologies (ACAT), 6 National Highway Traffic Safety Adminis- advanced emergency braking system (AEBS), tration (NHTSA), 6 2 naturalistic evaluation, 5 near point, 21 ballistic movement, 22, 44 near-crash behavior, 4 behavioral variability, 4, 11 between-subject, 12 open-loop control, 5 within-subject, 12 optical expansion, 17 body slip angle, 15 optimal control theory, 20 control error, 20 perceptual cue, 21 control gain, 20 physical reaction point, 18 control loss, 15 pre-crash scenario,
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