Improvement of Driving Simulator Eye Tracking Software

The proposed work focuses on improving the eye tracking tools used with the HumanFIRST driving simulator. Eye tracking is an important feature for simulation-based projects. It allows researchers to understand where participants are focusing their visual attention while driving the simulator. Currently the eye tracking system is capable of providing a nearly continuous record of the direction in which the driver is looking with respect to real-world coordinates. However, this by itself does not give any information about what objects the driver was looking at. For example, it may be necessary to identify when and for how long they were focused on a real-world object (e.g., gauge cluster, center stack, or mirror) or an object in the simulated world (e.g., a car, road sign, or potential hazard). To collect this type of information, additional processing is necessary. Current methods to process the raw eye tracking data are time intensive, requiring a human to go through eye tracking data and system video by hand in order to extract useful data. This project seeks to examine the process by which the eye tracking data is processed in order to identify and implement software tools to make the process more efficient. One option that will be examined is to use existing eye tracking video output from the system that superimposes a dot representing the driver's gaze location on video from a forward-facing scene camera. It may be possible to process this video using computer vision algorithms in order to detect what object (e.g., a car, road sign, etc.) is under the dot and use that to calculate gaze locations and fixation times by object. A second method that will be examined is to use the eye tracking system's 3D vision vector and apply that to known information about the simulator's state (i.e., the position and speeds of different elements relative to the driver/vehicle). That information could then be combined to calculate with what and for how long the driver's line of sight intersects with virtual and real-world objects. A successful project outcome will be the software tools and associated documentation for reducing or eliminating the amount of human intervention when processing the eye tracking data.


  • English


  • Status: Active
  • Funding: $52789
  • Contract Numbers:



  • Sponsor Organizations:

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    Roadway Safety Institute

    University of Minnesota
    Minneapolis, MN  United States  55455

    Office of the Assistant Secretary for Research and Technology

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

    Stearns, Amy

  • Performing Organizations:

    University of Minnesota

    Department of Mechanical Engineering
    111 Church Street SE
    Minneapolis, MN  United States  55455
  • Principal Investigators:

    Davis, Brian

  • Start Date: 20180322
  • Expected Completion Date: 20190531
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01664328
  • Record Type: Research project
  • Source Agency: Roadway Safety Institute
  • Contract Numbers: DTRT13-G-UTC35, CTS-2018065
  • Files: UTC, RiP
  • Created Date: Mar 27 2018 12:46PM