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| Title | Deep Reinforcement Learning Based Vehicular Cooperative Control Algorithm At Signal-Free Intersection; [基于深度强化学习的无信号交叉口车辆协同控制算法] |
|---|---|
| ID_Doc | 18044 |
| Authors | Jiang M.-Z.; Wu T.-H.; Zhang L. |
| Year | 2022 |
| Published | Journal of Transportation Engineering and Information, 20, 2 |
| DOI | http://dx.doi.org/10.19961/j.cnki.1672-4747.2021.11.021 |
| Abstract | Aiming at the traffic efficiency of intelligent connected vehicles passing through a signal-free intersection in future smart cities, in this paper we propose a progressive value-expectation estimation multi-agent cooperative control (PVE-MCC) algorithm based on deep reinforcement learning. First, the PVE-MCC algorithm designs a progressive value-expectation estimation (PVE) strategy based on progressive learning by dynamically varying the value expectation learning goal from short-term to long-term changes. The value function network is guaranteed to gradually and continuously learn, and the strategic network is prevented from falling into a local optimal solution. Second, the PVE-MCC algorithm combines the PVE strategy with the generalized advantage estimation algorithm to improve the convergence accuracy and stability of the algorithm. Third, the PVE-MCC algorithm jointly takes traffic efficiency, safety, and comfort as the optimization objective, and designs a multi-objective reward function to improve the performance of multi-agent collaborative control. In addition, the "deadlock" phenomenon that easily occurs at signal-free intersections constitutes a remarkable challenge for multi-vehicle cooperative control. In response to this problem, the PVE-MCC algorithm based on the linked list ring detection algorithm designs a heuristic detection-cracking intervention strategy for the "deadlock" to ensure the safety of the intersection. Finally, we present a simulation experimental platform for a two-way six-lane signal-free intersection for verification. The experimental results show that the PVE-MCC algorithm improves the traffic flow rate by 30.47%, the single-vehicle efficiency by 95.56%, and the comfort by 53.82% compared with existing schemes. © 2022 Southwest Jiaotong University. All rights reserved. |
| Author Keywords | cooperative control; intelligent connected vehicles; intelligent transportation; reinforcement learning; signal-free intersection |
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