NeuralParker: 不規則な駐車環境のための強化学習プランナー
NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
配送車両などが不規則な環境で任意の姿勢に駐車するための強化学習ベースのハイブリッドプランナーを提案。環境全体の障害物と境界を目標相対表現で符号化し、長距離の経路計画を可能にした。
著者: Zihan Wang, Bai Huang, Yang Guan, Xiao Li, Haoyu Xu, Naizheng Wang, Shengbo Eben Li
分類: cs.RO, cs.LG
原文アブストラクト
Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-specified pose in an irregular bounded environment from a distant start. Existing learning-based parking planners often rely on local observations, which can restrict long-range route reasoning. To address this problem, we present NeuralParker, a reinforcement learning-based hybrid planner for arbitrary-pose parking. NeuralParker encodes full-environment obstacle and boundary geometry in a target-relative vertex representation, allowing the policy to retain route-defining context throughout the approach. It further couples a learned curvature--length arc policy with an in-loop terminal ensemble that selects from diverse cubic Hermite connections using a curvature-regularized cost. We also establish factorial and long-range route-choice benchmarks to evaluate planning success and trajectory quality. Experiments on these benchmarks show that NeuralParker achieves higher planning success and better overall trajectory quality than the evaluated baselines, while ablation studies support the benefits of the target-relative global representation and terminal ensemble. Finally, a real-vehicle evaluation confirms that the planner transfers effectively to real delivery-vehicle perception at a working parking site, planning successfully at low computational cost.