A Computationally Efficient Bi-Level Coordination Framework for CAVs at Unsignalized Intersections
In this article, the authors investigate cooperative vehicle coordination for connected and automated vehicles (CAVs) at unsignalized intersections. To support high traffic throughput while reducing computational complexity, the authors present a novel collision region model and decompose the optimal coordination problem into two sub-problems: centralized priority scheduling and distributed trajectory planning. Then, the authors propose a bi-level coordination framework which includes: i) a Monte Carlo Tree Search (MCTS)-based high-level priority scheduler aims to find high-quality passing orders to maximize traffic throughput, and ii) a priority-based low-level trajectory planner that generates optimal collision-free control inputs. Simulation results demonstrate that our bi-level strategy achieves near-optimal coordination performance, comparable to state-of-the-art centralized strategies, and significantly outperform the traffic signal control systems in terms of traffic throughput. Moreover, our approach exhibits good scalability, with computational complexity scaling linearly with the number of vehicles.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2024, IEEE.
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Authors:
- Luo, Jiping
- Zhang, Tingting
- Zhang, Qinyu
- Publication Date: 2024-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1868-1878
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 73
- Issue Number: 2
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Computer models; Connected vehicles; Intelligent vehicles; Trajectory control; Unsignalized intersections
- Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01909788
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 26 2024 8:52AM