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

作　　者：苏越阳 姚迪 毕经平[1,2] SU YUE-yang;YAO Di;BI Jing-ping(University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院大学,北京100049 [2]中国科学院计算技术研究所,北京100190

出　　处：《电子学报》2025年第1期182-192,共11页Acta Electronica Sinica

基　　金：国家自然科学基金(No.62002343)。

摘　　要：车辆轨迹异常检测为各种位置信息服务提供了重要的安全保障,基于机器学习的方法作为主流检测方法已经被广泛地应用于交通、军事等各个领域.然而受限于噪声标签问题,现有的异常检测方法在实际应用中性能不佳.为解决这个问题,本文提出了一种基于噪声标签重加权的车辆轨迹异常检测方法(noise label ReWeighting-based vehicle Trajectory Anomaly Detection,RW-TAD).该方法采用自监督的方式构建样本权重估计模块,通过计算轨迹的生成概率评估给定标签的可信度.然后使用基于加权损失的检测模型判定异常轨迹.在训练过程中,RW-TAD模型使用基于双层损失的协同优化机制联合学习样本权重估计模块和异常检测模块.实验结果表明该方法可以有效缓解噪声标签样本对模型训练的干扰,取得了较好的性能.相比于已有的方法,RW-TAD在检测准确率和性能稳定性上都有很大的提升.Vehicle trajectory anomaly detection provides important security support for various location-based services.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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]