Parking occupancy estimation holds significant potential in facilitating parking resource management and mitigating traffic congestion. Existing approaches employ robotic systems to detect the occupancy status of individual parking spaces and primarily focus on enhancing detection accuracy through perception pipelines. However, these methods often overlook the crucial aspect of robot path planning, which can hinder the accurate estimation of the entire parking area. In light of these limitations, we introduce the problem of informative path planning for parking occupancy estimation using autonomous vehicles and formulate it as a Partially Observable Markov Decision Process (POMDP) task. Then, we develop an occupancy state transition model and introduce a Bayes filter to estimate occupancy based on noisy sensor measurements. Subsequently, we propose the Monte Carlo Bayes Filter Tree, a computationally efficient algorithm that leverages progressive widening to generate informative paths. We demonstrate that the proposed approach outperforms the benchmark methods in diverse simulation environments, effectively striking a balance between optimality and computational efficiency.

翻译：停车占用率估计在促进停车资源管理和缓解交通拥堵方面具有重要潜力。现有方法采用机器人系统检测单个停车位的占用状态，主要侧重于通过感知流程提高检测精度。然而，这些方法往往忽略了机器人路径规划这一关键环节，这可能导致对整个停车区域的准确估计受限。针对这些不足，我们提出基于自主车辆的停车占用率信息路径规划问题，并将其形式化为部分可观测马尔可夫决策过程（POMDP）任务。随后，我们构建了占用状态转移模型，并引入贝叶斯滤波器，基于含噪声的传感器测量值估计占用状态。进而，我们提出蒙特卡洛贝叶斯滤波器树算法，这是一种利用渐进式扩展生成信息路径的高效计算方法。实验证明，该方法在多种仿真环境中均优于基准方法，有效平衡了最优性与计算效率。