Examining accident reports involving autonomous vehicles in California

Favarò, Francesca; Nader, Nazanin; Eurich, Sky O.; Tripp, Michelle; Varadaraju, Naresh · 2017 · OpenAlex-citations

DOI: 10.1371/journal.pone.0184952

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Summary

This study analyzes accident reports involving autonomous vehicles (AVs) tested on public roads in California to evaluate safety dynamics and inform future regulations. Motivated by the rapid expansion of AV technology and the need for transparency, the authors examine data mandated by the California Department of Motor Vehicles (CA DMV). The research aims to identify failure modes, collision types, and contributing factors to determine if current semi-autonomous systems are effectively reducing accident risks compared to human-driven vehicles. The researchers conducted an in-depth analysis of the CA DMV’s “Report of Traffic Accidents Involving Autonomous Vehicles” database, covering the period from September 2014 to March 2017. While the DMV also tracks disengagements (instances where control reverts to a human driver), this study focuses specifically on the 26 reported accidents resulting in property damage or injury. The data was sourced from five manufacturers: Google (Waymo), General Motors (Cruise Automation), Nissan, Delphi, and BMW. The authors reconstructed accident dynamics using detailed reports, including vehicle status, location, and damage descriptions, and compared these findings against National Highway Traffic Safety Administration (NHTSA) statistics for conventional vehicles. The results indicate that Google accounted for 84% of the reported accidents, a disparity attributed to its significantly larger fleet size and mileage compared to other manufacturers. Analysis of the collision dynamics revealed that 62% of the accidents were rear-end collisions where the AV was struck from behind. Notably, in only one instance was the AV responsible for hitting another vehicle from behind, and this occurred while the vehicle was in manual mode. The study found that AVs were rarely involved in head-on or side-impact collisions, suggesting that autonomous systems effectively prevent these accident types. The authors interpret the high rate of rear-end collisions involving AVs as a sign that the technology successfully mitigates other risks, leaving rear-end impacts as the primary remaining failure scenario. The significance of these findings lies in their implications for AV regulation and development. The data suggests that semi-autonomous vehicles are capable of preventing the majority of accident typologies associated with human error, such as distraction-related crashes. However, the prevalence of rear-end collisions highlights a specific vulnerability that manufacturers must address. The study supports the argument that careful analysis of real-world testing data can better inform regulatory frameworks than restrictive bans, providing evidence that AVs are reducing overall accident risks while identifying specific areas for technological improvement.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

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• incidence prevalence

Information type

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• Empirical Findings: crash risk outcomes
• Methodological Resource: dataset resource