Autonomous Delivery Vehicle as a Disruptive Technology: How To Shape the Future With a Focus on Safety?

Details

• Creators:
  Das, Subasish ; Tsapakis, Ioannis ; Elgart, Zachary ; Elgart, Zachary ; Kutela, Boniphace ; Vierkant, Valerie ; Li, Eric
• Corporate Creators:
  Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Virginia Tech Transportation Institute ; Virginia Polytechnic Institute and State University
• 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 ; State of Texas
• Subject/TRT Terms:
  • Autonomous vehicles
  • Decision support systems
  • Delivery vehicles
  • Performance measurement
  • Traffic safety
  • Safety Autonomous delivery vehicles
  • Disruptive technology
• Publication/ Report Number:
  05-087
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Safety through Disruption (Safe-D) University Transportation Center (UTC)
• Abstract:
  The National Highway Traffic Safety Administration recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these vehicles are occupant less, frequent stops and mobility among residential neighborhoods cause safety-related concerns. There is consequently a need for a comprehensive safety impact analysis of ADVs. This study examined the safety implications and safety impacts of ADVs by using novel approaches. This research prepared several datasets such as fatal crash data, aggregated ADV trips and trajectories, and real-world crash data from the scenario design for an ADV-related operational design domain. Association rules mining was applied to the datasets to identify significant patterns. This study generated a total of 80 association rules that provide risk patterns associated with ADVs. The rules can be used as prospective benchmarks to examine how rule-based risk patterns can be reduced by ADVs that replace human-driven trips.
• Format:
  PDF
• Funding:
  69A3551747115
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/64569/dot_64569_DS1.pdf
• File Type:
  [PDF - 2.36 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:a16685265d3c02e50e2eb13e47fcef0cd2c2e0888f692652bb7861ac5670b8f9db62b0a9eaf07f5527f52a94ea1e7245ef3250ab0caf56892c207aafb8fe2aeb