电子学报2025，Vol.53Issue(1)：182-192,11.DOI:10.12263/DZXB.20230568
基于噪声标签重加权的车辆轨迹异常检测方法
A Vehicle Trajectory Anomaly Detection Method Based on Noise Label Re-Weighting
摘要
Abstract
Vehicle trajectory anomaly detection provides important security support for various location-based servic-es.Machine learning-based methods,as the mainstream detection methods,have been widely applied in various fields such as transportation and military.However,due to the problem of noise labels,existing anomaly detection methods have poor performance in practical applications.To solve this problem,this paper proposes a vehicle trajectory anomaly detection method based on noise label re-weighting(RW-TAD).This method uses a self-supervised approach to construct a sample weight estimator,which evaluates the credibility of given labels by calculating the probability of trajectory generation.Then,a detector based on weighted loss is used to detect anomalous trajectories.During the training process,the RW-TAD model uses a collaborative optimization strategy based on a dual-layer loss to jointly learn the sample weight estimator and the detector.Experimental results show that this method can effectively alleviate the interference of noisy samples on model training and achieve good performance.Compared with existing methods,it has greatly improved in detection accuracy and performance stability.关键词
异常检测/轨迹数据/噪声标签学习/路网数据/重加权Key words
anomaly detection/trajectory/noise label learning/road network/re-weighting分类
信息技术与安全科学引用本文复制引用
苏越阳,姚迪,毕经平..基于噪声标签重加权的车辆轨迹异常检测方法[J].电子学报,2025,53(1):182-192,11.基金项目
国家自然科学基金(No.62002343) National Natural Science Foundation of China(No.62002343) （No.62002343）
