Using Connected Intelligent Transportation To Enhance Vulnerable Road User Safety

Details

• Creators:
  Huang, Zilin ; Wan, Zhengyang ; Sheng, Zihao ; Ahn, Sue ; Noyce, David A. ; Chen, Sikai
• Corporate Creators:
  University of Wisconsin--Madison. Department of Civil and Environmental Engineering. Geological Engineering Program ; Center for Connected and Automated Transportation (CCAT)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Connected vehicles
  • Vulnerable road users
  • Traffic safety
  • Testing
  • Transportation
  • Wireless communication systems
  • Vehicle to everything communications
  • Virtual reality
  • Forecasting
  • Real-world Test
• Publication/ Report Number:
  GR000034956
• DOI:
  https://doi.org/10.21231/W55Q-GM71
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report, Jan 2024-Dec 2025
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  This project leverages connected intelligent transportation technologies to enhance the safety of vulnerable road users (VRUs) within evolving urban road systems. This project targets three fundamental barriers: the lack of high-fidelity experimental platforms, localization failures in GNSS-denied areas, and insufficient trajectory prediction for heterogeneous agents. Our goal is to develop an integrated cooperative system that combines virtual reality, wireless communication, and physics-informed learning. To achieve this, we develop Sky-Drive, a distributed multi-agent simulation platform that enables human-in-the-loop interaction testing. Furthermore, the project implements a cellular Vehicle-to-Everything (C-V2X)-based cooperative localization framework to ensure lane-level accuracy and a kinematics-aware multigraph attention network for precise motion forecasting. The output of this project provides a comprehensive technological foundation for enabling the safe and equitable coexistence of autonomous vehicles (AVs) and VRUs in mixed traffic environments.
• Format:
  PDF
• Funding:
  69A3552348305
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89376/dot_89376_DS1.pdf
• File Type:
  [PDF - 40.34 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:1e39b87bc5b0f2db3b0442739828ef0e2d78e67b0a722e8a93a962902a7144b3302da30c77258e41f83d51df0c401b2e80103ad4b6dee72ba32f712b2b570ce1