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.

翻译：随着车对车通信技术的进步，自动驾驶车辆能够以编队形式实现互联与协作，从而降低事故风险、成本及能源消耗。车辆编队带来的显著优势正日益引起自动化与人工智能领域的关注，然而针对编队超车场景的研究仍较为有限。为解决该问题，本文提出基于NoisyNet的多智能体深度Q学习算法，其中NoisyNet被用于增强环境探索能力。通过综合考虑超车、速度、碰撞、时距及跟随车辆等因素，设计了一种领域定制化的奖励函数，以实现安全且高速的编队超车。仿真结果表明，所提方法能够在不同交通密度场景下成功完成超车任务。