Analyzing Automated Vehicle Crashes in California: Application of a Bayesian Binary Logit Model

Automated vehicles (AVs) represent an opportunity to reduce the number of crashes by eliminating driver error as safety studies reveal human error contributes in 94% of crashes. However, existing literature lacks an understanding of the contributing factors of AV crashes. To provide insights on these crashes, this study created a unique database from California Department of Motor Vehicles (DMV) 66 manufacturer-reported Traffic Collision Reports (OL 316). The gathered information includes text mining of narratives in the reports and answers to close-ended crash questions. Results indicate that AV technology was faulty once of the 66 crashes (1.52%); the most frequent AV crash type is rear-ended (58%; N=38)—but in all cases, except one manually driven AV, the AV was struck by a conventional vehicle. This noteworthy outcome motivated the authors to analyze rear-end collisions by estimating assorted Bayesian models rigorously. The results indicate that most AV collisions occurred in the fully automated mode (65.2%), and the odds of AVs being struck were higher compared to vehicle takeover before impact and conventionally driven vehicles. Furthermore, the odds of an AV being rear-ended were substantially higher at an intersection than any other location, owing to the complexity of movements and conflicts at intersections. Given a crash, AV-involved rear-end crashes were more likely on one-way streets and when AVs were in motion. Within the constraints of the available data, the results highlight risk factors, given AV-involved crashes on public roadways. This study helps the authors understand the interactions of AVs and human-driven conventional vehicles in complex urban environments.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
  • Corporate Authors:

    Transportation Research Board

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  • Authors:
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: References; Tables;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01698164
  • Record Type: Publication
  • Report/Paper Numbers: 19-05567
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:47AM