Autonomous vehicle scheduling at signal-free intersections based on deep reinforcement learning

Jiaoqiong He; Ruru Hao; Tianhao Guan

doi:10.1117/12.3024391

14 February 2024 Autonomous vehicle scheduling at signal-free intersections based on deep reinforcement learning

Jiaoqiong He, Ruru Hao, Tianhao Guan

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301853 (2024) https://doi.org/10.1117/12.3024391
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

Coordinating autonomous vehicles (AVs) at signal-free intersections has emerged as a critical area of research in intelligent transportation. This paper presents a novel vehicle scheduling strategy based on multi-agent deep reinforcement learning (MADRL) to enhance traffic efficiency and reduce collision rates at signal-free intersections. The strategy incorporates the use of virtual lane techniques to simplify the management of vehicle trajectory conflicts. Furthermore, vehicle platooning techniques are employed to alleviate the computational burden on the reinforcement learning controller by treating multiple AVs as a single agent, thereby optimizing training performance. The MADRL approach is adopted to facilitate platoon coordination and enhance intersection traffic efficiency. Through comprehensive simulations, the proposed approach demonstrates its effectiveness in improving traffic efficiency, driving comfort, and safety at signal-free intersections.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaoqiong He, Ruru Hao, and Tianhao Guan "Autonomous vehicle scheduling at signal-free intersections based on deep reinforcement learning", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301853 (14 February 2024); https://doi.org/10.1117/12.3024391

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KEYWORDS

Education and training

Design

Safety

Unmanned vehicles

Simulations

Mathematical optimization

Decision making

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