基于噪声标签重加权的车辆轨迹异常检测方法

苏越阳; 姚迪; 毕经平

doi:10.12263/DZXB.20230568

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基于噪声标签重加权的车辆轨迹异常检测方法

学术论文 | 更新时间：2025-12-08

- 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
- 电子学报 2025年53卷第1期页码：182-192
- 作者机构：
  
  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
- 作者简介：
  
  [ "苏越阳男，1992 年 12 月出生，内蒙古包头人.中国科学院计算技术研究所助理研究员.主要研究方向为时序数据挖掘、异常检测以及RAG系统分析. E-mail: suyueyang@ict.ac.cn" ]
  [ "姚迪男，1990年8月出生，河南省许昌人.中国科学院计算技术研究所副研究员.主要研究方向为时空数据挖掘和深度学习. E-mail: yaodi@ict.ac.cn" ]
  [ "毕经平女，1974年7月出生，山东省泰安人.中国科学院计算技术研究所研究员.主要研究方向为网络测量、路由、虚拟化、SDN和大数据分析. E-mail: bjp@ict.ac.cn" ]
- 基金信息：
  
  国家自然科学基金(62002343)
- DOI：10.12263/DZXB.20230568
  中图分类号： TP391;
- 收稿：2023-06-20，
  
  修回：2024-01-25，
  
  纸质出版：2025-01-25
- 稿件说明：
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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(01): 182-192.

SU YUE-yang, YAO Di, BI Jing-ping. A Vehicle Trajectory Anomaly Detection Method Based on Noise Label Re-Weighting[J]. Acta Electronica Sinica, 2025, 53(01): 182-192.
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(01): 182-192. DOI：10.12263/DZXB.20230568

SU YUE-yang, YAO Di, BI Jing-ping. A Vehicle Trajectory Anomaly Detection Method Based on Noise Label Re-Weighting[J]. Acta Electronica Sinica, 2025, 53(01): 182-192. DOI：10.12263/DZXB.20230568

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.

Abstract

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

Keywords

references

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