用离线强化学习实现安全高效通用的自动泊车
SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
- 端到端离线强化学习，融合感知与决策
- 在CARLA仿真中成功率最高，泛化能力更强
- 专为泊车场景构建数据集，支持交互式泊车
自动泊车是实现城市自动驾驶安全高效的关键环节。然而，非结构化环境和动态交互给自动泊车带来重大挑战。为此，我们提出SEG-Parking，一种新型端到端离线强化学习框架，实现交互感知的自动泊车。特别地，构建了一个专用泊车数据集，涵盖无对向车辆干扰和存在对向车辆交互的复杂场景。基于该数据集，预训练一个目标条件状态编码器，将融合感知信息映射到隐空间；随后，采用保守正则化项优化离线强化学习策略，惩罚分布外动作。在高保真CARLA仿真器中开展闭环实验，对比结果表明，本框架在成功率和分布外场景泛化能力上均表现最优。相关数据集与源代码将在论文录用后公开。
原文摘要 · Abstract (English)
Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant challenges to autonomous parking tasks. To address this problem, we propose SEG-Parking, a novel end-to-end offline reinforcement learning (RL) framework to achieve interaction-aware autonomous parking. Notably, a specialized parking dataset is constructed for parking scenarios, which include those without interference from the opposite vehicle (OV) and complex ones involving interactions with the OV. Based on this dataset, a goal-conditioned state encoder is pretrained to map the fused perception information into the latent space. Then, an offline RL policy is optimized with a conservative regularizer that penalizes out-of-distribution actions. Extensive closed-loop experiments are conducted in the high-fidelity CARLA simulator. Comparative results demonstrate the superior performance of our framework with the highest success rate and robust generalization to out-of-distribution parking scenarios. The related dataset and source code will be made publicly available after the paper is accepted.
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