Connected and Automated Vehicle Based Intersection Maneuver Assist Systems (CAVIMAS) and Their Impact on Driver Behavior, Acceptance, and Safety

Details

• Creators:
  Pradhan, Anuj K. ; Jeong, Heejin ; Bao, Shan
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; University of Massachusetts at Amherst ; University of Illinois at Chicago ; University of Michigan, Dearborn
• Corporate Contributors:
  United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Autonomous vehicles
  • Behavior
  • Connected vehicles
  • Driver support systems
  • Driving simulators
  • Intersections
  • Human factors Automated vehicles
• Publication/ Report Number:
  UMTRI-2020-3
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (May 2017 – April 2018)
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  Intersection crashes can be potentially mitigated by leveraging deployments of vehicle-to-infrastructure (V2I) and vehicle-to- vehicle (V2V) safety management solutions. However, it is equally critical that these deployments are undertaken in tandem with interventions based on human factors evidence relating to the content and presentation of such solutions. This driving simulator study designed and evaluated a conceptual system - Connected and Automated Vehicle based Intersection Maneuver Assist Systems (CAVIMAS) - aimed at assisting drivers with intersection maneuvers by leveraging connected infrastructure and providing real-time guidance and warnings and active vehicle controls. Results indicate that human factors considerations for the design and deployment of such systems remain paramount, given the findings related to drivers’ trust and acceptance of these systems as measured via surveys and by examining actual driving behaviors.
• Format:
  PDF
• Funding:
  69A3551747105
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/58947/dot_58947_DS1.pdf
• File Type:
  [PDF - 1.98 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:ea1f985f80cb40a1d906e02aa3a2b7ef3c7b6bef425285dd33e6a00d24d14017b7ec451113eb2f2f5814f7039331eb290537ef55b23a0f30ddcfe5d7c207e93a