Cooperative Perception of Connected Vehicles for Safety

This project develops vision-based cooperative perception and accident (crash) avoidance trajectory plans in dynamic environments for two connected vehicles in which the ego vehicle would face a potentially unseen hazard ahead but could receive safety-critical information from a vehicle in front and estimate/predict the trajectory of the potential hazard. There are several challenging technical problems in this V2V and V2X communications environment, aside from the communication itself. Among them are the accurate establishment of the relative position of the involved vehicles and their collective situation relative to the target (which could be a vulnerable road user or another vehicle); the decision of what constitutes a safety-critical information/data and when and how to pass (exchange) them with the ego vehicle to be beneficial for safety; passing of only safety-critical data and trajectories without having to pass extensive video data between two cooperating partners (vehicles); and how to best determine the final trajectories of the ego vehicle and the corresponding cooperating vehicle in order to avoid a potential/imminent collision with the target. To address these challenges and questions, a combination of algorithms and approaches will be developed based on probabilistic random trees (or similar) approaches and other intelligent algorithms to find the optimum ways of cooperating among the two vehicles and defining their forthcoming safe trajectories. The results will be tested in a traffic emulation environment with autonomous connected mobile robots. The methods and approaches will be equally applicable to real-life full vehicles upon further development and testing.


  • English


  • Status: Active
  • Funding: $290895
  • Contract Numbers:


  • 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 (Safe-D)

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

    Glenn, Eric

  • Performing Organizations:

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061
  • Principal Investigators:

    Eskandarian, Azim

  • Start Date: 20201001
  • Expected Completion Date: 20230510
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01754803
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Oct 17 2020 12:55AM