Trent Victor, PhD, Director of Safety Research & Best Practices at Waymo, presented Safety Research at Waymo at the ITS Transportation Seminar on Friday, Sept. 23, 2022.
Abstract: This talk will focus on providing insights on key safety research topics needed to support Autonomous Vehicle development and deployment. It will use publications found on waymo.com/safety to illustrate some of the issues where foundational research is currently, or should be, focused. Ideally, the talk will facilitate an exploration of mutual research interests.
Bio: Dr Trent Victor is director of safety research and best practices at Waymo, an autonomous driving technology company with a mission to make it safe and easy for people and things to get where they're going. Trent oversees safety research to support safety evaluations and strategic initiatives; provides safety expertise in the area of collision data analysis, collision causation, injury severity assessments, and naturalistic driving data research and analysis; represents safety issues within Waymo; and coordinates the development of best practices within Waymo and external industry standard-setting groups. Trent has published extensively in the field of crash avoidance and autonomous driving safety research (over 100 papers, 36 patents, >5000 citations). Prior to Waymo, he was senior technical leader at the Volvo Cars Safety Centre, adjunct professor in driver behavior at Chalmers, and adjunct professor at the University of Iowa. He has received top awards such as the US Government award for Safety Engineering Excellence, Volvo Cars Technology & Innovation Award, and the Jerome H. Ely Human Factors (journal) Article Award.
Related papers from the seminar:
Safety Framework
Webb, N., Smith, D., Ludwick, C., Victor, T., Hommes, Q., Favaro, F., Ivanov, G., Daniel, T. (2020). Waymo's safety methodologies and safety readiness determinations. arXiv:2011.00054
Waymo public road safety data
Schwall, M., Daniel, T., Victor, T., Favaro, F., Hohnhold, H. (2020). Waymo public road safety performance data. arXiv:2011.00038
Fatal Crash Reconstructions
Scanlon, J.M., Kusano, K.D., Daniel, T., Alderson, C., Ogle, A., Victor. (2021). Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain. Accident Analysis & Prevention 163, 106454. 10.1016/j.aap.2021.106454
Naturalistic Response Times
Engström, J., Liu S-Y, Dinparastdjadid, A. and Simoiu, C. 2022. Modeling Road User Response Timing in Naturalistic Traffic Conflicts: A surprise-based framework. arxiv.org/abs/2208.08651
Collision Severity Modelling
McMurry, T. L., Cormier, J. M., Daniel, T., Scanlon, J. M., & Crandall, J. R. (2021). An omni-directional model of injury risk in planar crashes with application for autonomous vehicles. Traffic injury prevention, 22(sup1), S122-S127. 10.1080/15389588.2021.1955108
Maximum Injury Potential
Kusano, K. and Victor, T. (In Press). Methodology for Determining Maximum Injury Potential for Automated Driving System Evaluation. AAAM 66th Annual Scientific Conference 2022, October 11-14.
Fatigue Risk Management
F Favaro, K Hutchings, P Nemec, L Cavalcante, T Victor. (2022). Waymo's Fatigue Risk Management Framework: Prevention, Monitoring, and Mitigation of Fatigue-Induced Risks while Testing Automated Driving Systems. arXiv:2208.12833