Prediction of Vehicle Trajectories at Intersections Using Inverse Reinforcement Learning

The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems which aim to identify critical events such as near crash situations and traffic violations. In a connected environment, important information about these critical events can be communicated to road users or the infrastructure to avoid or mitigate potential crashes. Intersections require special attention in this context because they are hotspots for accidents and involve numerous and complex interactions between road users. The proposed project will investigate approaches to and methodologies for vehicle trajectory prediction at intersections, and identify current challenges and opportunities. The project will also explore the applicability of inverse reinforcement learning (IRL) in developing trajectory prediction models. IRL is a machine learning framework for Learning from Demonstration (LfD) which allows for the learning of the series of actions that dictate driver behavior from expert demonstration. Trajectory prediction, while normally posed as a prediction problem, can readily be cast as a control problem given that the problem is essentially equivalent to finding how to drive a vehicle the way a human driver would. This is crucial in enabling us to use IRL to solve it. The IRL framework makes certain assumptions about the problem to be solved that, when these assumptions hold, improve the generalizability of models that are based on. The research team hopes that by employing this framework they will be able to build models that better capture the behavior of human drivers at intersection.


  • English


  • Status: Completed
  • Funding: $150000
  • 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:

    Jahangiri, Arash

  • Start Date: 20200515
  • Expected Completion Date: 20210515
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01741719
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Jun 1 2020 1:31PM