NoisyNetマルチエージェント深層Q学習による車群追い越し制御
Multi-vehicle Platoon Overtaking Using NoisyNet Multi-Agent Deep Q-Learning Network
車車間通信で連携する自動運転車群の追い越しを実現するため、NoisyNetで探索を改善したマルチエージェント深層Q学習と専用報酬関数を提案し、様々な交通密度で安全な追い越しに成功した。
著者: Lv He
分類: cs.RO
原文アブストラクト
With the recent advancements in Vehicle-to-Vehicle communication technology, autonomous vehicles are able to connect and collaborate in platoon, minimizing accident risks, costs, and energy consumption. The significant benefits of vehicle platooning have gained increasing attention from the automation and artificial intelligence areas. However, few studies have focused on platoon with overtaking. To address this problem, the NoisyNet multi-agent deep Q-learning algorithm is developed in this paper, which the NoisyNet is employed to improve the exploration of the environment. By considering the factors of overtake, speed, collision, time headway and following vehicles, a domain-tailored reward function is proposed to accomplish safe platoon overtaking with high speed. Finally, simulation results show that the proposed method achieves successfully overtake in various traffic density situations.