Development and Evaluation of a Surrounding Vehicle Identification System for Mixed Traffic Cooperative Platooning
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 12
Abstract
To facilitate cooperation among connected automated vehicles (CAVs), such as cooperative platooning or cooperative lane changes in mixed traffic comprising CAVs, connected human-driven vehicles (CHVs), and conventional (unconnected) human-driven vehicles (HDVs), an ego CAV needs to discern which surrounding vehicles are connected (CAVs or CHVs) and which are unconnected (HDVs). Therefore, this paper introduces a surrounding vehicles identification system (SVIS) designed to enable CAVs to identify the connectivity status of surrounding vehicles. Unlike a previously developed preceding vehicle identification system that only identifies the connectivity of its immediately preceding vehicle, the proposed SVIS generalizes this identification to nearby surrounding vehicles. Furthermore, the proposed SVIS significantly reduces the error in the connected vehicle identification by integrating distance and the magnitude of velocity for matching instead of distance-only matching used by the previous system. The SVIS is a must to enable CAVs to form platoons via lane changes after a connected vehicle is detected in an adjacent lane. We compared a newly proposed distance-plus-speed-matching approach with a previously developed distance-only-matching approach using Next Generation Simulation (NGSIM) data. Through comparison, the efficacy of the proposed distance-plus-speed-matching SVIS is demonstrated to be considerably superior to that of the distance-only-matching SVIS. Given that a reliable and robust method for identifying surrounding connected vehicles is key to the successful formation of cooperative adaptive cruise control (CACC) platoons and cooperative lane changes, the proposed distance-plus-speed-matching SVIS would help quickly and efficiently form platoons in a mixed traffic environment.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This research is supported by the National Science Foundation (NSF) under Grant No. CMMI-2009342 and Toyota Motor North America R&D. This paper’s contents reflect the authors’ views, who are responsible for the facts and accuracy of the data presented herein.
Author contributions: B. Park: Study conception and design, Analysis and interpretation of results. S. Avedisov: Study conception and design, Analysis and interpretation of results. M. Imran: Simulation setup and implementation, Analysis and interpretation of results, Writing–original draft. Z. Mu: Simulation setup and implementation, Analysis and interpretation of results, Writing–original draft. All authors reviewed the results and approved the final version of the manuscript.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 12 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 3, 2024
Accepted: May 31, 2024
Published online: Sep 28, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 28, 2025
ASCE Technical Topics:
- Automatic identification systems
- Business management
- Computer networks
- Computing in civil engineering
- Detection methods
- Driver behavior
- Engineering fundamentals
- Highway transportation
- Human and behavioral factors
- Infrastructure
- Intelligent transportation systems
- Internet
- Methodology (by type)
- Practice and Profession
- Traffic engineering
- Traffic management
- Transportation engineering
- Transportation management
- Vehicles
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