用强化学习让自动驾驶车在不确定环境下更好协作规划。
Unveiling Uncertainty-Aware Autonomous Cooperative Learning Based Planning Strategy
- 基于SAC与GRU的强化学习框架，处理感知、通信和规划不确定性。
- 在CARLA仿真中表现优于基线方法，多场景下协作效率提升显著。
- 适合研究自动驾驶协同决策与不确定性建模的工程师和学者。
在未来的智能交通系统中，自动驾驶协同规划（ACP）成为提升多车交互效率与安全性的关键技术。然而，现有ACP策略难以全面应对感知、规划与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，以应对多种不确定性下的协同运动规划问题。具体而言，采用融合门控循环单元（GRUs）的软演员-评论家（SAC）算法，学习在规划、通信及感知不确定性导致的状态信息不完整条件下，时变的确定性最优动作。通过Car Learning to Act（CARLA）仿真平台验证了自动驾驶车辆实时动作的有效性。评估结果表明，所提DRLACP能够有效学习并执行协同规划，在多种场景下均优于其他基线方法，尤其在状态信息不完整的情况下表现更优。
原文摘要 · Abstract (English)
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.
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