Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles

While safety is the ultimate goal of designing connected automated vehicles (CAVs), in many instances, CAVs’ decisions do not match the expectations of human drivers (e.g., 3-second stopping rules versus rolling stops performed by human drivers). Such instances can lead to crashes/near-crashes; for instance, in 18 out of 26 crashes involving CAVs in California through February 2017, a CAV was rear-ended by a human driver at an intersection. Unfortunately, the state-of-the-art in CAV safety analysis is focused on actual and simulated miles driven, which are shown to be infeasible to apply after each update (software and/or hardware). Accordingly, this project is expected to bring insight from traffic safety analysis to develop a systematic approach for CAV safety evaluation. This project will identify the factors that contribute to crashes in mixed traffic with automated and human-driven vehicles through data analysis, simulation, and field tests. Moreover, it will develop measures and guidelines to minimize the risk of such crashes. The findings of this study are expected to significantly enhance the safety of operating CAVs.

• Record URL:
  https://www.vtti.vt.edu/utc/safe-d/index.php/projects/preventing-crashes-in-mixed-traffic-with-automated-and-human-driven-vehicles/

Language

• English

Project

• Status: Active
• Funding: $172648
• Contract Numbers:

  69A3551747115

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Safety through Disruption University Transportation Center

  Virginia Tech Transportation Institute
  Blacksburg, VA  United States  24060
• Project Managers:

  Harwood, Leslie

• Performing Organizations:

  Texas A&M Transportation Institute

  Texas A&M University System
  3135 TAMU
  College Station, TX  United States  77843-3135

  San Diego State University

  5500 Campanile Dr
  San Diego, CA  United States  92182
• Principal Investigators:

  Talebpour, Alireza

• Start Date: 20180501
• Expected Completion Date: 20190930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Connected vehicles; Crash causes; Data analysis; Drivers; Field tests; Intelligent vehicles; Simulation; Traffic safety; Vehicle mix
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01658901
• Record Type: Research project
• Source Agency: Safety through Disruption University Transportation Center
• Contract Numbers: 69A3551747115
• Files: UTC, RiP
• Created Date: Feb 2 2018 6:11PM