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。
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.
作者
杨宗儒
胡韫泽
刘士琪
关阳
吴伟
刘畅
YANG Zongru;HU Yunze;LIU Shiqi;GUAN Yang;WU Wei;LIU Chang(School of Advanced Manufacturing and Robotics,Peking University,Beijing 100871,China;School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China;State Key Laboratory of Intelligent Green Vehicle and Mobility.Beijing 100084,China)
出处
《汽车安全与节能学报》
北大核心
2026年第1期140-148,共9页
Journal of Automotive Safety and Energy
基金
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课题(KFZ2410)
北京市科技新星项目(2024048449)。
