スマート交差点におけるマルチエージェント強化学習に基づく協調自動運転
Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersections
路側機(RSU)が全体知覚とV2I通信を用いて交差点内の複数自動運転車を協調制御するシステムを提案し、二段階の強化学習(CQL+BCの事前学習とMAPPO+自己注意の微調整)で訓練、CARLA実験で高い成功率と頑健性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Taoyuan Yu, Kui Wang, Zongdian Li, Tao Yu, Kei Sakaguchi
分類: cs.RO, cs.MA, cs.SY, eess.SY
原文アブストラクト
Unsignalized intersections pose significant safety and efficiency challenges due to complex traffic flows. This paper proposes a novel roadside unit (RSU)-centric cooperative driving system leveraging global perception and vehicle-to-infrastructure (V2I) communication. The core of the system is an RSU-based decision-making module using a two-stage hybrid reinforcement learning (RL) framework. At first, policies are pre-trained offline using conservative Q-learning (CQL) combined with behavior cloning (BC) on collected dataset. Subsequently, these policies are fine-tuned in the simulation using multi-agent proximal policy optimization (MAPPO), aligned with a self-attention mechanism to effectively solve inter-agent dependencies. RSUs perform real-time inference based on the trained models to realize vehicle control via V2I communications. Extensive experiments in CARLA environment demonstrate high effectiveness of the proposed system, by: \textit{(i)} achieving failure rates below 0.03\% in coordinating three connected and autonomous vehicles (CAVs) through complex intersection scenarios, significantly outperforming the traditional Autoware control method, and \textit{(ii)} exhibiting strong robustness across varying numbers of controlled agents and shows promising generalization capabilities on other maps.
