California Autonomous Vehicle Crashes: Explanatory Data Analysis and Classification Tree

Autonomous Vehicle (AV) is an evolving technology with many capabilities and limitations. The main safety attribute of AVs is eliminating human drivers from the driving process with the promise to decrease road crashes drastically. AV field tests are being conducted in several states in the US and in other parts of the world. California Department of Motor Vehicle (DMV) has mandated all AV crashes and disengagement incidents being publicly reported by permit holders since 2014. Several different studies had used the CA DMV data to investigate different aspects of AVs especially road safety attributes. In this study, 234 CA DMV AV-related crashes (2017-2020) were examined. Explanatory Data Analyses indicated that rear-end and side-swipe were the main collision types and based on geographic distribution of crashes, the majority of them happened in a relatively small area in San Francisco bay area usually surrounding the permit holder headquarters. The classification tree using Chi-square Automatic Interaction Detector (CHAID) method was developed for AV-related crashes based on driving mode and AV movement, company (permit holder), road surface, other vehicle movement, intersection / control type, and crash time was identified as a significant contributing factor. Results, limitations, and potential future work were discussed in the context of the AVs and roadway safety.


  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 23p

Subject/Index Terms

Filing Info

  • Accession Number: 01763558
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-04068
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:05AM