Radar and LiDAR Fusion for Scaled Vehicle Sensing

R&D platforms and robust prototypes are essential for the development and demonstration of automated driving functions. Thousands of hours of safety and performance benchmarks must be met before an ADAS system is production-ready. However, full-scale test-beds are expensive to make, labor-intensive to design, and include inherent safety risks when testing. To reduce these risks and expenses, scaled prototypes can be developed to model system design and vehicle behavior in targeted driving scenarios. Scaled test-beds can improve the ease of safety-testing future ADAS systems and can help visualize test results and system limitations to audiences with varying technical backgrounds better than software simulations. However, though small-scaled vehicles can accommodate similar on-board systems to its full-scale counterparts, the performance of these systems (particularly sensors) can be affected. For example, as the vehicle scales down, the resolution from perception sensors and the performance for object tracking decreases, especially from mounted radars. With many automated driving functions relying on radar tracking, the small-scaled vehicle must overcome the reduced tracking ability at scale in order to provide safe and accurate system modeling. This project proposes a sensor fusion approach to augment radar data in a scaled environment that uses an off-the-shelf LiDAR as a high spatial resolution sensor. It is expected that, with this approach, radar tracking software (RTS) developed at full-scale will also reliably operate in small-scale environments. Successfully implementing radar and LiDAR fusion, system tests with scaled test-bed vehicles can then identify safety concerns in ADAS functions more quickly and efficiently, leading to faster and safer product development.


  • English


  • Status: Active
  • Funding: $142049
  • 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:

    Perez, Miguel

  • Start Date: 20191125
  • Expected Completion Date: 20210630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01730819
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Feb 11 2020 4:56PM