{"ID":2869846,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13956","arxiv_id":"2509.13956","title":"SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning","abstract":"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.","short_abstract":"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 (R...","url_abs":"https://arxiv.org/abs/2509.13956","url_pdf":"https://arxiv.org/pdf/2509.13956v1","authors":"[\"Zewei Yang\",\"Zengqi Peng\",\"Jun Ma\"]","published":"2025-09-17T13:28:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}