Cooperative Sensing of Vulnerable Road Users and Real-Time Response to Potential Collisions via Vehicle and Infrastructure Communication

Smith, Stephen F.; Dolan, John M; Chiu, Hsu-Kuang; Lyu, Yiwei

i

Cooperative Sensing of Vulnerable Road Users and Real-Time Response to Potential Collisions via Vehicle and Infrastructure Communication

2025-08-25
By Smith, Stephen F. ; Dolan, John M ; Chiu, Hsu-Kuang ; ...

File Language:

English

Details

Creators:

Smith, Stephen F. ; Dolan, John M ; Chiu, Hsu-Kuang ; Lyu, Yiwei
Corporate Creators:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
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:

Autonomous Vehicles Connected Vehicles Crash Avoidance Systems Machine Learning Pedestrian Detectors Traffic Safety Vehicle To Infrastructure Communications Vehicle To Vehicle Communications Vulnerable Road Users
Publication/ Report Number:

511
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report (July 1, 2024 – June 30, 2025)
Corporate Publisher:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
Abstract:

This report summarizes progress made on two problems of central importance to achieving increased safety in autonomous urban driving: (1) the development of end-to-end frameworks for cooperative perception, tracking and planning, and (2) the development of real-time strategies for collision avoidance when collisions are predicted. With respect to the first problem, a new framework that utilizes a multi-modal large language model (MLLM) has been developed and shown to outperform previous approaches to end-to-end autonomous navigation on a contemporary open-source data set. Progress on the second problem has led to an analysis of the effectiveness of several alternative conflict mitigation strategies. The report ends with a discussion of next steps toward refining and integrating these sets of results.
Format:

PDF
Funding:

69A3552344811, 69A3552348316
Collection(s):

University Transportation Centers Autonomous Vehicles
Main Document Checksum:

urn:sha-512:ba8c4875300c9a4166aa2684b2bd5396baedeb6a9b94eebf81b6efb2318a3fe9ae92cf011d26439b6f12a267f2bd97bc4b81266f4b0d44cab3953ad944a70bf2
Download URL:

https://rosap.ntl.bts.gov/view/dot/86527/dot_86527_DS1.pdf
File Type:

[PDF - 1.13 MB ]

File Language:

English

ON THIS PAGE

Details

ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.