TARGET setting for high severity collisions: tolerance-based assessment of risk for generalized event thresholds
DOI:
https://doi.org/10.55329/wxoa2712Keywords:
autonomous vehicles, impact speed, injury risk, Safe System, Vision ZeroAbstract
Vision Zero represents a road safety approach with aspirations toward eliminating serious and fatal injuries associated with traffic collisions. Given the well-described relationship between speed at impact and injury outcomes, many researchers have used a variety of methodological approaches to develop speed thresholds associated with human injury tolerance levels for serious and fatal injuries. The goal of this study was to present a framework based on state-of-the-art injury risk models using the latest field data and featuring biomechanically-relevant predictors in order to create safe impact speed thresholds. Tolerance-based Assessment of Risk for Generalized Event Thresholds (TARGET) values for safe speeds for several sets of the most commonly observed collision geometries and partners were estimated using previously-developed injury risk models. Consistent with prior literature, an injury tolerance level of 10% risk at the MAIS3+ severity level was evaluated given its association with high severity injury outcomes. Leveraging models built on German collision data for VRUs, the safe impact speed thresholds were 34 kph for pedestrians and 49 kph for cyclists and motorcyclists. Using models built on U.S. collision data for collisions involving passenger vehicles, the thresholds for closing speed were 99 kph for a frontal collision, 73 kph for a near-side collision, and 126 kph for a rear-end collision. The TARGET values established in this study are consistent with those previously developed and can serve as a validation of these previous studies. As an additional demonstrative, we highlighted other factors (increased age and vehicle seating position) that affect serious and fatal injury risk and were associated with decreased safe impact speed thresholds. This study used a data-driven approach, injury risk models with additional biomechanically-relevant predictors, and the most modern collision data to provide a more precise approach to quantify generalized speed thresholds associated with biomechanical tolerance for humans involved in automotive collisions. Given the relationships between speed and injury risk, reducing speed in a collision below these thresholds is key to mitigating serious and fatal injury outcomes. The objective injury risk approach used in this study enables traffic safety practitioners to determine the relative effect of related safety countermeasures on reaching the goals of Vision Zero and a Safe System Approach.
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Author Biographies
Eamon T. Campolettano, Waymo, the United States of America
Eamon Campolettano is a Senior Safety Research Engineer at Waymo. He received his PhD in Biomedical Engineering and Mechanics from Virginia Tech and has extensive experience in injury assessments. His research interests include injury risk model development, vulnerable road user safety, and injury biomechanics.
CRediT contribution: Conceptualization, Formal analysis, Methodology, Visualization, Writing—original draft.
John M. Scanlon, Waymo, the United States of America
John Scanlon is a Staff Safety Researcher at Waymo. He received his PhD in Biomedical Engineering and Mechanics from Virginia Tech. His work focuses on both the prospective assessment of injury risk and retrospective evaluation of system performance, and he is the project leader of ISO/PWI TS 25536 on ‘Retrospective safety performance assessment for Automated Driving Systems’.
CRediT contribution: Methodology, Writing—review & editing.
Timothy L. McMurry, Waymo, the United States of America
Timothy McMurry is a Senior Data Scientist at Waymo. He received his PhD in Mathematics from the University of California, San Diego. His research interests include the statistics of biomechanical injury and large-scale crash database analysis. He has extensive experience with large databases such as NASS-CDS and CISS.
CRediT contribution: Methodology, Validation, Writing—review & editing.
Kristofer D. Kusano, Waymo, the United States of America
Kristofer Kusano is a Staff Safety Researcher at Waymo. He received his PhD in Mechanical Engineering from Virginia Tech. His current focuses are on Automated Driving System safety, including high severity injury crash prevention, safety impact methodology, and scenario-based testing. He has previously developed highway automation systems while at Toyota.
CRediT contribution: Conceptualization, Methodology, Supervision, Writing—review & editing.
Trent Victor, Waymo, the United States of America
Trent Victor is Director of the Safety Research and Best Practices team at Waymo. Prior to joining Waymo, he was a senior technical leader at the Volvo Cars Safety Centre. He has published extensively in the field of crash avoidance and autonomous driving safety research (over 100 papers, 36 patents, over 5000 citations).
CRediT contribution: Supervision, Writing—review & editing.
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Copyright (c) 2025 Eamon T. Campolettano, John M. Scanlon, Timothy L. McMurry, Kristofer D. Kusano, Trent Victor
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