Collaborative Perception with Adaptive V2X Communication Support for Enhanced CAV Safety and Coordination

This research project aims to enhance the safety and coordination of connected and autonomous vehicles (CAVs) through collaborative perception supported by adaptive vehicle-to-everything (V2X) communication. By leveraging V2X technology, the project will enable CAVs to share sensor data, enhancing their ability to detect and track critical objects, such as pedestrians and cyclists, across complex environments. This collaborative approach not only improves object detection accuracy but also reduces uncertainty in high-risk scenarios, supporting the U.S. DOT's priority to reduce accidents involving CAVs and vulnerable road users. The project will also develop an adaptive network resource partitioning framework, ensuring V2X communication can dynamically adjust to changing data needs and maintain optimal performance. Through simulation and real-world validation, this study will evaluate the proposed framework's impact on CAV safety and performance in diverse driving scenarios. The results will guide the development of safer, more effective CAV systems by enhancing their perception and decision-making capabilities, paving the way for broader adoption of V2X-enabled autonomous vehicles. The project’s insights will be valuable for policymakers, technology developers, and transportation agencies looking to implement advanced CAV solutions that improve public safety and reliability.

Language

• English

Project

• Status: Active
• Funding: $166000
• Contract Numbers:

  69A3552348301

• 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:

  University of Massachusetts, Amherst

  Department of Civil and Environmental Engineering
  130 Natural Resources Road
  Amherst, MA  United States  01003
• Performing Organizations:

  University of Connecticut, Storrs

  Connecticut Transportation Institute
  270 Middle Turnpike, Unit 5202
  Storrs, CT  United States  06269-5202
• Principal Investigators:

  Miao, Fei

  Han, Song

• Start Date: 20240901
• Expected Completion Date: 20250831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Subprogram: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles
• Subject Areas: Data and Information Technology; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01935923
• Record Type: Research project
• Source Agency: New England University Transportation Center
• Contract Numbers: 69A3552348301
• Files: UTC, RIP
• Created Date: Nov 5 2024 11:41AM