In future intelligent transportation systems, autonomous cooperative planning (ACP), becomes a promising technique to increase the effectiveness and security of multi-vehicle interactions. However, multiple uncertainties cannot be fully addressed for existing ACP strategies, e.g. perception, planning, and communication uncertainties. To address these, a novel deep reinforcement learning-based autonomous cooperative planning (DRLACP) framework is proposed to tackle various uncertainties on cooperative motion planning schemes. Specifically, the soft actor-critic (SAC) with the implementation of gate recurrent units (GRUs) is adopted to learn the deterministic optimal time-varying actions with imperfect state information occurred by planning, communication, and perception uncertainties. In addition, the real-time actions of autonomous vehicles (AVs) are demonstrated via the Car Learning to Act (CARLA) simulation platform. Evaluation results show that the proposed DRLACP learns and performs cooperative planning effectively, which outperforms other baseline methods under different scenarios with imperfect AV state information.

翻译：在未来智能交通系统中，自主协同规划（ACP）作为一种提升多车交互效能与安全性的关键技术展现出广阔前景。然而，现有ACP策略仍无法全面应对感知、规划及通信等多重不确定性。为此，本文提出一种基于深度强化学习的自主协同规划（DRLACP）框架，旨在解决协同运动规划方案中的各类不确定性问题。具体而言，采用集成门控循环单元（GRUs）的柔性演员-评论家（SAC）算法，通过学习处理由规划、通信及感知不确定性所引发的不完备状态信息，从而确定时变最优动作。此外，通过Car Learning to Act（CARLA）仿真平台验证了自动驾驶车辆（AVs）的实时动作表现。评估结果表明，所提出的DRLACP框架能有效学习并执行协同规划，在不同自动驾驶车辆状态信息不完备的场景下均优于其他基线方法。