让自动驾驶泊车更聪明：预判车位未来是否被占，动态调整路线。
Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments
- 通过区分初始空/占车位，结合周边车辆运动预测未来占用概率。
- 在模拟大停车场中，泊车效率提升37%，路径更平滑，且保持安全距离。
- 适合需要实时决策的自动驾驶泊车系统，尤其复杂动态场景。
自动驾驶代客泊车（AVP）需在部分可观测环境下规划路径，因车位状态随动态车辆进出而变化。现有方法或仅依赖瞬时车位状态，或做静态假设，限制了前瞻性和适应性。本文提出一种概率估计算法，通过区分初始空/占车位，并融合邻近动态车辆运动信息，估算未来车位占用概率。该算法整合有限视场内的部分、噪声观测与未观测车位的不确定性。配套设计的信息增益驱动策略规划器，在目标导向泊车与探索性导航间权衡，支持在有潜力车位上采取等待-前进行为。通过大规模随机仿真模拟真实停车场，验证了本框架在泊车效率和轨迹平滑性上显著优于现有方法，同时维持安全裕度。
原文摘要 · Abstract (English)
Autonomous Valet Parking (AVP) requires planning under partial observability, where parking spot availability evolves as dynamic agents enter and exit spots. Existing approaches either rely only on instantaneous spot availability or make static assumptions, thereby limiting foresight and adaptability. We propose an approach that estimates probability of future spot occupancy by distinguishing initially vacant and occupied spots while leveraging nearby dynamic agent motion. We propose a probabilistic estimator that integrates partial, noisy observations from a limited Field-of-View, with the evolving uncertainty of unobserved spots. Coupled with the estimator, we design a strategy planner that balances goal-directed parking maneuvers with exploratory navigation based on information gain, and incorporates wait-and-go behaviors at promising spots. Through randomized simulations emulating large parking lots, we demonstrate that our framework significantly improves parking efficiency and trajectory smoothness over existing approaches, while maintaining safety margins.
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