Identifying Deviations from Normal Driving Behavior

Advanced driver assistance systems (ADAS) have significantly improved safety on today’s roadways but their impact may be limited by driver errors. Understanding and identifying these driver errors will require the integration of multi-domain datasets through predictive modeling and data integration approaches. The goals of this project are to identify relevant datasets for ADAS error prediction, evaluate modeling approaches for predicting driver errors during ADAS use, and developing models to proactively predict driver errors. Results from the project will be used to guide data collection system design at automakers and develop predictive modeling benchmarks.


  • English


  • Status: Active
  • Funding: $49493
  • 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:

    Texas A&M Transportation Institute (TTI)

    400 Harvey Mitchell Parkway South
    Suite 300
    College Station, TX  United States  77845-4375
  • Principal Investigators:

    Manser, Michael

  • Start Date: 20201020
  • Expected Completion Date: 20211015
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01769144
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Apr 6 2021 2:11PM