A comparison of patterns and contributing factors of ADAS and ADS involved crashes

Crashes involving Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been increasing in recent years. Understanding the characteristics of these crashes can guide the optimization of driving automation systems and the policies improving the safety of mixed traffic. However, due to the limited available data, the crashes of ADS- and ADAS-controlled vehicles are still under-investigated. Thus, utilizing the latest National Highway Traffic Safety Administration crash reports, the study explores the patterns and contributing factors of ADAS- and ADS-involved crashes. The sequences of events leading to crashes were extracted from the reports and then categorized into five clusters. Next, for incomplete records, a non-parametric imputation method was applied based on Random Forest. Finally, logistic regression models were built to explore the factors associated with the crashes. The results show that the automation level, speed limit, and vehicle speed are predictors of crash patterns. At the same time, the crash pattern, combined with incident time, roadway type, roadway surface, and vehicle model year are associated with crash outcomes (i.e. contact area and injury severity). The results indicate that further improvement of the ADS/ADAS control algorithms and driver education may be needed to improve the safety of mixed traffic.

• Record URL:
  • https://doi.org/10.1080/19439962.2023.2284175
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19439962
• Supplemental Notes:
  • © 2023 Taylor & Francis Group, LLC and The University of Tennessee 2023. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Yan, Song
  • Huang, Chunxi
  • He, Dengbo
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1061-1088
• Serial:
  • Journal of Transportation Safety & Security
  • Volume: 16
  • Issue Number: 9
  • Publisher: Taylor & Francis
  • ISSN: 1943-9962
  • EISSN: 1943-9970
  • Serial URL: http://www.tandfonline.com/loi/utss20#.U2qTJPldWCm

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Crash avoidance systems; Crash data; Driver support systems; Predictive models; Vehicle factors in crashes
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01933443
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 14 2024 9:01AM