Using Infrastructure to Boost Safety in a PNT World

Compelling opportunities exist to utilize roadside sensing systems for improving safety among vulnerable road users. Particularly with the advancements in technology, leveraging low-cost camera feeds, optionally fused with LiDAR or other technology, can be a highly effective sensing strategy for traffic safety improvements. In this research plan, the research team proposes to collect necessary data from pedestrian crossings on busy streets and establish strategies for deriving meaningful patterns and trends to investigate the following: (1) Occlusion, Navigation, and Pedestrian Safety: A range of traffic scenarios will be examined to identify those where sensing from infrastructure can significantly enhance safer navigation of nearby vehicles, especially when on-vehicle sensing technologies alone may fall short. Selected scenarios shall be demonstrated in a laboratory context. (2) Yielding Patterns: Through the collected footage, researchers will examine if there are consistent trends in driver yielding behavior at different locations and times of the day. Variations in yielding behavior shall be systematically investigated, examining potential disparities linked to both the socioeconomic context and the racial composition of neighborhoods. Furthermore, yielding discrepancies associated with specific vehicle types (such as luxury vehicles and large trucks) will be analyzed. Findings are anticipated to shed light on how future position, navigations, and timing (PNT) schemes can be designed to overcome related disparities. (3) Impact of Bus Stops: Researchers will investigate whether the presence of a bus stop near an intersection affects pedestrian visibility and driver behavior. This information could inform the placement of bus stops and strategies for transit-related PNT in relation to pedestrian crossings. (4) Amplifying Resilience: The placement of roadside sensing for the purpose of enhancing systemwide PNT schemes, and safety of all road users also has the dual effect of serving as a basis for detection of navigation error or external spoofing affecting on-vehicle systems. Strategies shall be explored to heighten this resilience while maintaining a suitable level of functionality. To accomplish the proposed goals, researchers will collaborate with the Texas Advanced Computing Center (TACC) to leverage advanced image analysis techniques, employing the YOLO (You Only Look Once) object detection algorithm. This algorithm will allow researchers to suitably detect and track pedestrians, vehicles, and other relevant objects in the surveillance footage and produce anonymized and highly aggregated output that can be further analyzed with novel strategies. In addition, other resources available to researchers associated with connected vehicle technologies shall be used to assess the effectiveness of a range of safety improvement strategies. In conclusion, this research plan aims to harness the power of roadside sensing technologies to enhance safety of vulnerable road users, and inform urban planning strategies. Researchers intend to uncover valuable insights that can positively impact road safety and pedestrian experiences in environments with significant vehicle automation and advanced PNT implementations.


  • English


  • Status: Active
  • Funding: $240000
  • 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:

    Ohio State University Center for Automotive Research

    930 Kinnear Road
    Columbus, OH  United States  43212
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    University of Texas at Austin

    Austin, TX  United States  78712
  • Principal Investigators:

    Bhat, Chandra

  • Start Date: 20231030
  • Expected Completion Date: 20240830
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01901165
  • Record Type: Research project
  • Source Agency: Center for Automated Vehicle Research with Multimodal Assured Navigation
  • Contract Numbers: 69A3552348327
  • Files: UTC, RIP
  • Created Date: Dec 1 2023 5:01AM