Reference Machine Vision for ADAS Functions

Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning (LDW) and Lane Keep Assist (LKA) systems are used effectively. However, these systems are not yet popular because the systems are not robust due, in part to the lack of suitable standards for pavement markings that enable reliable functionality of the sensor system. The objective of this project is to develop a reference Lane Detection (LD) system that will provide a benchmark for evaluating different lane markings and perception algorithms. The project will also validate the effectiveness of lane markings as well as the vision algorithms through a systematic development of LD metrics, and testing of LD algorithms in a robust test/vehicle environment.


  • English


  • Status: Active
  • Funding: $340160
  • 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, College Station

    Texas A&M University System
    3135 TAMU
    College Station, TX  United States  77843-3135
  • Principal Investigators:

    Rathinam, Sivakumar

  • Start Date: 20190101
  • Expected Completion Date: 20210515
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01701520
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Apr 8 2019 3:48PM