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汽车安全与节能学报

汽车安全与节能学报 ›› 2026, Vol. 17 ›› Issue (1): 140-148.DOI: 10.3969/j.issn.1674-8484.2026.01.015

• 智能驾驶与智慧交通 • 上一篇    

停车占位状态估计的分布式主动感知的路径规划

杨宗儒1(), 胡韫泽1, 刘士琪2, 关阳2, 吴伟3, 刘畅1,*()   

  1. 1.北京大学 先进制造与机器人学院，北京 100871，中国
    2.清华大学 车辆与运载学院，北京 100084，中国
    3.智能绿色车辆与交通全国重点实验室，北京 100084，中国
  • 收稿日期:2025-11-12 修回日期:2025-12-15 出版日期:2026-02-28 发布日期:2026-03-19
  • 通讯作者: 刘畅，研究员。E-mail：changliucoe@pku.edu.cn。
  • 作者简介:杨宗儒（1999—），男（汉），吉林，硕士研究生。E-mail：yzr@stu.pku.edu.cn。
  • 基金资助:
    国家自然科学基金项目(62203018);智能绿色车辆与交通全国重点实验室开放基金(KFZ2410);北京市科技新星项目(2024048449)

Distributed active perception path planning for the estimation of parking occupancy status

YANG Zongru1(), HU Yunze1, LIU Shiqi2, GUAN Yang2, WU Wei3, LIU Chang1,*()   

  1. 1. School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China
    2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    3. State Key Laboratory of Intelligent Green Vehicle and Mobility. Beijing 100084, China
  • Received:2025-11-12 Revised:2025-12-15 Online:2026-02-28 Published:2026-03-19

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摘要：

为实时估计可用于多辆自动驾驶汽车的停车场车位占用状态，提出一种路径规划算法。该算法具有分布式主动感知，命名为“多车Monte Carlo Bayes滤波树（MV-MCBFT）”。构建车位状态的概率转移模型，设计多源更新的Bayes滤波融合机制，结合次模函数最大化原理，提出预估观测驱动的序贯式前馈协同运动规划策略。结果表明：在熵下降比例与估计准确率等指标上，MV-MCBFT取得了与遍历算法一致的近似最优结果，而耗时仅为遍历算法的1%；与随机游走算法相比，本文MV-MCBFT的熵下降比提升了43.70%；估计准确率提升了51.43%。从而，本文方法提升了停车位状态估计效果。

关键词: 自动驾驶汽车, 信息路径规划, Bayes滤波, 停车占位状态估计

Abstract:

A path planning algorithm, named “multi-vehicle Monte Carlo Bayes filter tree (MV-MCBFT)”, was proposed for the distributed active perception of multiple autonomous vehicles to estimate the occupancy status of parking lots in real time. A sequential, feed-forward cooperative motion planning strategy driven by predicted observations was proposed by constructing a probabilistic state-transition model for parking lots, with designing a multi-source Bayesian filtering fusion mechanism, and with incorporating submodular maximization principles. The results show that the MV-MCBFT achieves near-optimal performance consistent with the traversal algorithm in terms of entropy reduction ratio and estimation accuracy, while consuming only 1% of the runtime required by the traversal algorithm. The MV-MCBFT has the entropy reduction ratio by 43.70% and the estimation accuracy by 51.43% comparing with the random-walk algorithm. Therefore, the proposed method enhances the effectiveness of parking lot state estimation.

Key words: autonomous vehicles, informative path planning, Bayes filter, parking occupancy status estimation

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