Road User-Automated Vehicle Expectancies, Interactions, and Safety

Details

• Creators:
  Andriola, Cesar L. ; Chitturi, Madhav V. ; Noyce, David A. ; Chen, Sikai ; Ahn, Soyoung (Sue)
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; University of Wisconsin--Madison
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Automated vehicle control
  • Safety
  • Vulnerable road users
  • Data analysis
  • Human powered vehicles
  • Pedestrians
  • Surveys
  • Cyclists
  • Crash analysis
  • Traffic violations
  More +
• Publication/ Report Number:
  119
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (September 2024 - May2026)
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  Trust in the Automated Vehicles (AVs) is predicated on their ability to conform to expectancies of the road users and community. Rapid advancement and adoption of AVs raises the important question of how well AVs can respect road users' expectancies and coexist in harmony. The objective of this research is to identify patterns in AV and HDV crashes as well as the role of expectancy violations in AV crashes using sequences of events. Data from 555 AV crashes and 39,270 HDV crashes were used. Overall data analysis shows that intersections are much more challenging to AVs than to HDVs. Other interesting observations are the higher proportion of rear end crashes and the much lower severity for AV crashes. The first observable result is the presence of clusters representing pedestrian and bicyclist crashes only in the HDV data. A second trend is the difference in crash patterns between AVs and HDVs during right and left turns. For these movements, while rear end is the most common crash type for AV, angle is the most common for HDV crashes. Another relevant observation is the greater presence of crashes involving red light/stop sign violations by a second vehicle involved in AV crashes. Finally, the results show three groups with contexts particular to AV crashes. Narratives of AV Crashes representing Turning Movements and Vehicle Crossings indicate expectancy violation played a role in these crashes. AVs violated expectancy of HDVs with hesitation/caution in permissive turning movements and not yielding to emergency vehicles. HDVs violated expectancy of AVs by running stop signs or red lights at intersections. Systematic human-in-the-loop (HIL) experiments simulating AV-HDV-Pedestrian and AV-HDV interactions should be conducted to probe expectancy violations and safety impacts in future research.
• Format:
  PDF
• Funding:
  69A3552348305
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93184/dot_93184_DS1.pdf
• File Type:
  [PDF - 5.74 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:f6ee8c1b990e873c2c5fef6d5279a5f2559a26c6bd31ef90694a1598e9e1a38a523277ee2d52852fc574976f90d5e2e7c95b665c52d10ca1ef6d3a566ae6f302