Real-Time and Secure Analysis of Pedestrian Data for Connected Vehicles CVs
Although Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection, this safety is hindered when pedestrians do not carry hand-held devices (e.g., Dedicated short-range communication (DSRC) and 5G enabled cell phone) to communicate with connected vehicles nearby. To overcome this limitation, in this project, traffic cameras at a signalized intersection were used to accurately detect and locate pedestrians via a vision-based deep learning technique to generate safety alerts in real-time about possible conflicts between vehicles and pedestrians. The contribution of this project lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds). The research team developed a pedestrian alert safety system (PASS) to generate a safety alert of an imminent pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a signalized intersection. The approach estimates the location and velocity of a pedestrian more accurately than existing DSRC-enabled pedestrian hand-held devices. A connected vehicle application, the Pedestrian in Signalized Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS. Numerical analyses show that the vision-based PASS is able to satisfy the accuracy and latency requirements of pedestrian safety applications in a connected vehicle environment.
Language
- English
Project
- Status: Completed
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Contract Numbers:
69A3551747117
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590Clemson University
110 Lowry Hall
Box 340911
Clemson, SC United States 29634-0911 1600 Harden Street
Columbia, South Carolina United States 29204 -
Managing Organizations:
Clemson University
110 Lowry Hall
Box 340911
Clemson, SC United States 29634-0911 -
Project Managers:
Apon, Amy
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Performing Organizations:
Clemson University
110 Lowry Hall
Box 340911
Clemson, SC United States 29634-0911 1600 Harden Street
Columbia, South Carolina United States 29204 -
Principal Investigators:
Apon, Amy
Comert, Gurcan
Chowdhury, Mashrur
- Start Date: 20171001
- Expected Completion Date: 20200601
- Actual Completion Date: 20200601
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Connected vehicles; Crosswalks; Machine learning; Machine vision; Pedestrian safety; Warning systems
- Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors;
Filing Info
- Accession Number: 01884937
- Record Type: Research project
- Source Agency: Center for Connected Multimodal Mobility
- Contract Numbers: 69A3551747117
- Files: UTC, RIP
- Created Date: Jun 14 2023 3:28PM