Modeling Automated Vehicle Crashes with a Focus on Vehicle At-Fault, Collision Type, and Injury Outcome
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 6
Abstract
Automated vehicle (AV) technology is expected to make roads safer. However, until recently only a handful of studies could test such hypotheses due to limited access to testing data. This study contributes to the literature by jointly analyzing the associated factors of three interrelated outcome variables—vehicle at fault, collision type, and injury outcome in AV-involved crashes. We use Bayesian networks to analyze the manually extracted data from reports of 333 AV-involved crashes that occurred in California between January 2017 and October 2021. The summary statistics indicate that rear-end collisions are the dominant (63.5%), while AVs are at fault for a small proportion of crashes (14.4%), and a majority of crashes (84.4%) are noninjury. The joint inferences of the Bayesian networks show that irrespective of the collision type, when the AV is at fault, the chance of the physical injury in a crash increases significantly. Further, the chance of an AV being at fault seems higher in parking locations, and during driving at wet pavements in unclear weather. The chances of AV rear-end collisions are lower in the parking lot, and when nonvehicular participants are involved but increase in high traffic flow roadways. We also find that the likelihood of physical injury is higher at high-speed locations, intersections, and wet pavements. These insights suggest specific areas (unsignalized intersections, less structured right-of-way rules, and wet pavements) where technological improvements could enhance the safety performance of AVs.
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Data Availability Statement
Some data used in this study were extracted from California Department of Motor Vehicles and they are open-sourced through the website (California-DMV 2021). Some or all data, models, or code generated or used during the study are available in a repository online (California-DMV 2021) in accordance with funder data retention policies. Some models or code may be made available by direct request to the authors.
Acknowledgments
Authors would like to acknowledge the California Department of Motor Vehicles for the open-source AV-involved crashes used in this study.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 6 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 16, 2021
Accepted: Feb 2, 2022
Published online: Mar 22, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 22, 2022
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