Evaluation of Autonomous Vehicles and Smart Technologies for Their Impact on Traffic Safety and Traffic Congestion

The research team proposes to evaluate the impact of smart driver technologies and autonomous vehicles on driver performance and traffic safety, and model the effects of these technologies on surprise traffic disruptions, thus addressing Topic 1-1, Technology and Mobility under Theme 1 of the Pacific Southwest Region 9 University Transport Center’s Request for Proposal. It is well known that driver error is a major determinant of traffic accidents, and a major determinant of driver performance is currently driver distraction and inattention due to the proliferation of new technologies in the automobile. Task-relevant technologies such adaptive cruise control, intersection collision avoidance and dynamic route guidance are expected to reduce congestion, freeway delay, and improve traffic safety. These technologies have been categorized by the National Highway Traffic Safety Administration (NHTSA) in terms of the level of autonomy in the vehicle, ranging from Level 0 (no autonomous vehicle control) to level 4 (complete autonomous vehicle control). To be effective, these technologies must be designed to avoid potential human-automation problems that are associated with automated systems in other domains such as aviation (e.g., mode control errors, reduced situation awareness, mistrust in automation, workload transitions). Therefore, the team proposes to evaluate these known consequences of autonomous systems in aviation for their effects on driver safety. The team will use this information to predict changes in crash rates and subsequent surprise traffic congestion for different levels of automation. In effect, the team will leverage the expertise and experience of the PI and Co-PI on automated systems in aerospace applications to the domain of driver performance and system outcomes. The specific objectives are as follows: (1) Determine from a review of existing research on operator inattention, situation awareness and trust in automation, as a function of levels of automation the potential impact on driver performance and subsequent traffic crash risk and surprise congestion. (2) Develop and run a driving simulation to identify driver performance costs as the level of automation increases from manual driving (L0) to fully automated travel (L3-L4). Determine the extent to which driver performance is affected by situation awareness, workload, trust in automation and inattention. (3) Modelling the effects identified in the driving simulation on potential changes in vehicle crash rates and surprise traffic congestion associated with negative automation effects. By meeting these objectives, the team will contribute important information for the design of autonomous vehicles and infrastructure needed to support them. The team expects, therefore, to present the results of their work at conferences in human factors, and journal articles in human factors and traffic safety.


    • English


    • Status: Active
    • Funding: $100000
    • Contract Numbers:

      Project #: MT-17-11

    • Sponsor Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626

      Pacific Southwest Region 9 UTC

      650 Childs Way
      Los Angeles, CA  United States  90089

      Department of Transportation

      1200 New Jersey Avenue, SE
      Washington, DC  United States  20590

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Brinkerhoff, Cort

    • Principal Investigators:

      Miles, James

    • Start Date: 20180101
    • Expected Completion Date: 20181231
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01652749
    • Record Type: Research project
    • Source Agency: National Center for Metropolitan Transportation Research
    • Contract Numbers: Project #: MT-17-11
    • Files: UTC, RiP
    • Created Date: Nov 30 2017 5:10PM