用深度强化学习实现高精度自动泊车，比传统方法更稳更快。
Automated Parking Trajectory Generation Using Deep Reinforcement Learning
- 基于SAC算法构建连续动作控制框架，实现精细车辆调度。
- 仿真中成功率高、泊车时间短，能有效应对动态障碍物。
- 适合自动驾驶研发人员参考，尤其关注泊车系统优化者。
自主泊车是现代自动驾驶系统的关键技术，需在复杂环境中具备高精度、强适应性和高效性。本文提出一种基于软动作-评论家（Soft Actor-Critic, SAC）算法的深度强化学习（DRL）框架，用于优化自主泊车任务。SAC是一种具有熵正则化的离策略方法，特别适用于连续动作空间，可实现精细车辆控制。将泊车任务建模为马尔可夫决策过程（MDP），通过熵最大化平衡探索与利用，训练智能体以最大化累积奖励。系统融合多传感器输入构成高维状态空间，利用SAC的双评论器网络和策略网络实现稳定学习。仿真结果表明，基于SAC的方法在成功率、降低操作时长和动态障碍物处理方面均优于传统规则方法及其他DRL算法，展现出在自主泊车中的应用潜力，并为实际落地奠定基础。
原文摘要 · Abstract (English)
Autonomous parking is a key technology in modern autonomous driving systems, requiring high precision, strong adaptability, and efficiency in complex environments. This paper proposes a Deep Reinforcement Learning (DRL) framework based on the Soft Actor-Critic (SAC) algorithm to optimize autonomous parking tasks. SAC, an off-policy method with entropy regularization, is particularly well-suited for continuous action spaces, enabling fine-grained vehicle control. We model the parking task as a Markov Decision Process (MDP) and train an agent to maximize cumulative rewards while balancing exploration and exploitation through entropy maximization. The proposed system integrates multiple sensor inputs into a high-dimensional state space and leverages SAC's dual critic networks and policy network to achieve stable learning. Simulation results show that the SAC-based approach delivers high parking success rates, reduced maneuver times, and robust handling of dynamic obstacles, outperforming traditional rule-based methods and other DRL algorithms. This study demonstrates SAC's potential in autonomous parking and lays the foundation for real-world applications.
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