Investigation of Emerging Sensing and AI/ML Technologies to Enhance the Safety of Vulnerable Roadway Users at Signalized Intersection

Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network cameras, and artificial intelligence/machine learning (AI/ML) algorithms to capture the movements and behaviors of vulnerable road users (VRUs). By evaluating pedestrian demand, including the volume and characteristics of pedestrian traffic, this research aims to assess and improve the safety of intersections. This project will start with a comprehensive study of the state-of-the-art methods of VRU data collection, image- and LiDAR-based VRU object detection and classification, and dynamic VRU trajectory estimation methods. Next, a candidate study intersection will be reviewed and selected for the sensor installation and data collection. The LiDARs and Cameras will be synchronized with the field processing unit and the retrieved data will be transferred and saved to be further analyzed. In the model development process, three traffic data collection framework will be designed: a roadside LiDAR-based VRU data collection, video-based VRU data collection, and an integrated framework.

• Record URL:
  • https://tbd.ctr.utexas.edu/research-product/investigation-of-emerging-sensing-and-ai-ml-technologies-to-enhance-the-safety-of-vulnerable-roadway-users-at-signalized-intersection/

Language

• English

Project

• Status: Active
• Funding: $371,410.00
• Contract Numbers:

  69A3552344815

  69A3552348320

• 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:

  Center for Understanding Future of Travel Behavior and Demand

  University of Texas
  Austin, TX  United States 
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  City College of New York

  Civil Engineering, Steinman T-127
  140th Street and Convent Avenue
  New York, NY  United States  10031
• Principal Investigators:

  Li, Yiqiao

• Start Date: 20240601
• Expected Completion Date: 20260531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Cameras; Data collection; Laser radar; Machine learning; Pedestrian detection; Signalized intersections; Traffic safety; Vulnerable road users
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01954939
• Record Type: Research project
• Source Agency: Center for Understanding Future of Travel Behavior and Demand
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: May 13 2025 7:09PM