量子化行動表現を用いたオフライン強化学習による信頼性の高い駐車ポリシーの学習
Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representations
LiDAR特徴を目標姿勢に適応させ、ウェイポイント列を離散トークンに量子化して保守的Q学習を行うオフライン強化学習により、対話を考慮した自動駐車を実現し、CARLAで最高の成功率と未見スロットへの転移を示した。
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著者: Zewei Yang, Zengqi Peng, Jun Ma
分類: cs.RO
原文アブストラクト
Parking is a routine yet safety-critical task for autonomous vehicles operating in urban environments. However, cluttered and weakly structured parking spaces, compounded by the interactive uncertainty from surrounding vehicles, hinder reliable maneuver generation. To address these challenges, we develop a waypoint-level offline reinforcement learning framework for interaction-aware autonomous parking. Specifically, a dedicated parking dataset is constructed from hierarchical expert rollouts with rotational waypoint augmentation, covering both non-interactive scenarios and interactive ones. The policy is then conditioned on a compact state representation, in which LiDAR-based obstacle features are adapted to the target pose via feature-wise linear modulation. A state-conditioned tokenizer further quantizes continuous waypoint sequences into discrete action tokens, over which conservative Q-learning is performed to suppress value overestimation on poorly supported actions. Extensive closed-loop experiments are conducted in the high-fidelity CARLA simulator. The proposed framework attains the highest parking success rate among all baselines and transfers reliably to unseen parking slots.