Vehicle trajectory anomaly detection based on multi-level spatio-temporal interaction dependency

doi:10.11772/j.issn.1001-9081.2025020234

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (2): 604-612.DOI: 10.11772/j.issn.1001-9081.2025020234

• Multimedia computing and computer simulation • Previous Articles

Vehicle trajectory anomaly detection based on multi-level spatio-temporal interaction dependency

Feng HAN¹, Yongfeng BU¹, Haoxiang LIANG²(), Shuwen HUANG¹, Zhaoyang ZHANG¹(), Shijie SUN³

^1.School of Information Engineering，Chang’an University，Xi’an Shaanxi 710064，China
^2.School of Electronics and Control Engineering，Chang’an University，Xi’an Shaanxi 710064，China
^3.Institute of Data Science and Artificial Intelligence，Chang’an University，Xi’an Shaanxi 710064，China

Received:2025-03-10 Revised:2025-05-26 Accepted:2025-05-28 Online:2025-06-10 Published:2026-02-10
Contact: Haoxiang LIANG, Zhaoyang ZHANG
About author:HAN Feng， born in 2001， M. S. candidate. His research interests include computer vision， anomaly detection.
BU Yongfeng， born in 1998， Ph. D. candidate. His research interests include computer vision， intelligent transportation.
LIANG Haoxiang， born in 1995， Ph. D.， lecturer. His research interests include traffic video analysis， abnormal trajectory modeling. Email:lhx@chd.edu.cn
HUANG Shuwen， born in 2001， M. S. candidate. Her research interests include computer vision， 3D visual grounding.
ZHANG Zhaoyang， born in 1984， Ph. D.， lecturer. His research interests include traffic incident detection and analysis， deep learning. Email:zhaoyang_zh@chd.edu.cn
SUN Shijie， born in 1989， Ph. D.， associate professor. His research interests include computer vision， object tracking， pose estimation.
Supported by:
National Key Research and Development Program of China(2023YFB4301800);Postdoctoral Fellowship Program of CPSF(GZC20241447);China University Industry-Academia-Research Innovation Fund — New Generation Information Technology Innovation Project(2023 IT 079);Fundamental Research Funds for the Central Universities — Chang’an University(300102244202)

基于多层次时空交互依赖的车辆轨迹异常检测

韩锋¹, 卜永丰¹, 梁浩翔²(), 黄舒雯¹, 张朝阳¹(), 孙士杰³

^1.长安大学信息工程学院，西安 710064
^2.长安大学电子与控制工程学院，西安 710064
^3.长安大学数据科学与人工智能研究院，西安 710064

通讯作者: 梁浩翔,张朝阳
作者简介:韩锋（2001—），男，山西吕梁人，硕士研究生，CCF会员，主要研究方向：计算机视觉、异常检测
卜永丰（1998—），男，山西运城人，博士研究生，CCF会员，主要研究方向：计算机视觉、智能交通
梁浩翔（1995—），男，陕西西安人，讲师，博士，主要研究方向：交通视频分析、异常轨迹建模 Email:lhx@chd.edu.cn
黄舒雯（2001—），女，广西桂平人，硕士研究生，CCF会员，主要研究方向：计算机视觉、三维视觉定位
张朝阳（1984—），男，陕西咸阳人，讲师，博士，主要研究方向：交通事件检测与分析、深度学习Email:zhaoyang_zh@chd.edu.cn
孙士杰（1989—），男，河南商丘人，副教授，博士，主要研究方向：计算机视觉、目标跟踪、位姿估计。
基金资助:
国家重点研发计划项目(2023YFB4301800);国家资助博士后研究人员计划项目(GZC20241447);中国高校产学研创新基金-新一代信息技术创新项目(2023 IT 079);长安大学中央高校基本科研业务费资助项目(300102244202)

Abstract

Abstract:

To address the complexity and dynamic nature of vehicle trajectory anomaly detection in intelligent transportation systems， a novel method named DSTGRU （Dynamic Spatio-Temporal Gated Recurrent Unit） was proposed on the basis of Multi-level Spatio-Temporal Interaction dependency Dynamic Graph （MSTIDG）. In DSTGRU， by constructing dynamic graphs for short-term and long-term spatio-temporal interaction dependencies， the complex interactions between vehicles were captured effectively. In this process， the Multi-level Spatio-temporal interaction feature Fusion Bidirectional Gate Recurrent Unit （MSF-BiGRU） module was introduced to fuse multi-level spatio-temporal features， so as to integrate spatio-temporal information at different scales， thereby alleviating conflicts in shared information extraction and enhancing the model’s robustness， which improved the ability to identify anomalous trajectories. Experimental results demonstrate that DSTGRU outperforms the existing methods significantly on the TrackRisk and HighD datasets， achieving Pre@100 of 0.90 and 0.89， respectively， and AUROC of 0.913 and 0.827， respectively. Compared to existing advanced methods， DiffTAD and ImDiffusion， DSTGRU ranks first in multiple evaluation metrics. Additionally， DSTGRU exhibits strong robustness in complex scenarios， and identifies anomalous behaviors accurately， providing a solution for trajectory anomaly detection in intelligent transportation systems.

Key words: vehicle trajectory anomaly detection, multi-level spatio-temporal feature fusion, dynamic graph model, deep learning, intelligent transportation system

摘要：

针对智能交通系统中车辆轨迹异常检测的复杂性和动态性，提出一种基于多层次时空交互依赖的动态图（MSTIDG）的车辆轨迹异常检测方法DSTGRU（Dynamic Spatio-Temporal Gated Recurrent Unit）。DSTGRU通过构建短期和长期时空交互依赖的动态图，有效地捕捉车辆间的复杂交互关系。在这个过程中，引入多层次时空交互特征融合（MSF-BiGRU）模块融合多层次时空特征，以在不同尺度上融合时空信息，从而缓解共享信息提取时的冲突并增强模型的鲁棒性，进而提升对异常轨迹的识别能力。实验结果表明，DSTGRU在TrackRisk和HighD数据集上的异常检测精度显著优于现有方法DiffTAD与ImDiffusion，Pre@100分别达到了0.90和0.89，AUROC分别达到了0.913和0.827。与现有方法对比，DSTGRU在多项评价指标上均排名第一。此外，DSTGRU在复杂场景中表现出较强鲁棒性，并能准确识别异常行为，为智能交通系统中的轨迹异常检测提供了解决方案。

关键词: 车辆轨迹异常检测, 多层次时空特征融合, 动态图模型, 深度学习, 智能交通系统

CLC Number:

TP391.41

Feng HAN, Yongfeng BU, Haoxiang LIANG, Shuwen HUANG, Zhaoyang ZHANG, Shijie SUN. Vehicle trajectory anomaly detection based on multi-level spatio-temporal interaction dependency[J]. Journal of Computer Applications, 2026, 46(2): 604-612.

韩锋, 卜永丰, 梁浩翔, 黄舒雯, 张朝阳, 孙士杰. 基于多层次时空交互依赖的车辆轨迹异常检测[J]. 《计算机应用》唯一官方网站, 2026, 46(2): 604-612.

Figures/Tables 14

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属性	值
数据记录总时长/h	12
车辆行驶记录距离/m	525~700
车道数量	2~4
车辆总数	76 194
乘用车数量	58 656
商用车数量	17 538
车辆记录总行驶时间/h	328
车辆总行驶距离/km	33 439
隧道内平均车速分布/（km·h ^-¹）	70~80
高速公路平均车速分布/（km·h ^-¹）	90~120
轨迹点数据采样频率/s	0.3
轨迹数量	684 630

属性	值
数据记录总时长/h	12
车辆行驶记录距离/m	525~700
车道数量	2~4
车辆总数	76 194
乘用车数量	58 656
商用车数量	17 538
车辆记录总行驶时间/h	328
车辆总行驶距离/km	33 439
隧道内平均车速分布/（km·h ^-¹）	70~80
高速公路平均车速分布/（km·h ^-¹）	90~120
轨迹点数据采样频率/s	0.3
轨迹数量	684 630

模型	TrackRisk				HighD
模型	Pre@100（↑）	Pre@300（↑）	Avg Pre（↑）	AUROC（↑）	Pre@100（↑）	Pre@300（↑）	Avg Pre（↑）	AUROC（↑）
CVM	0.18	0.143	0.095	0.607	0.12	0.078	0.081	0.533
LTI	0.24	0.208	0.099	0.631	0.20	0.182	0.092	0.542
TOP-EYE	0.32	0.308	0.211	0.742	0.22	0.190	0.156	0.602
Seq2Seq	0.61	0.448	0.142	0.765	0.50	0.418	0.127	0.627
STGAE	0.21	0.192	0.097	0.648	0.46	0.443	0.180	0.675
ImDiffusion	0.78	0.641	0.293	0.812	0.67	0.568	0.252	0.788
DiffTAD	0.88	0.681	0.298	0.902	0.82	0.736	0.263	0.803
DSTGRU	0.90	0.723	0.348	0.913	0.89	0.848	0.299	0.827

模型	TrackRisk				HighD
模型	Pre@100（↑）	Pre@300（↑）	Avg Pre（↑）	AUROC（↑）	Pre@100（↑）	Pre@300（↑）	Avg Pre（↑）	AUROC（↑）
CVM	0.18	0.143	0.095	0.607	0.12	0.078	0.081	0.533
LTI	0.24	0.208	0.099	0.631	0.20	0.182	0.092	0.542
TOP-EYE	0.32	0.308	0.211	0.742	0.22	0.190	0.156	0.602
Seq2Seq	0.61	0.448	0.142	0.765	0.50	0.418	0.127	0.627
STGAE	0.21	0.192	0.097	0.648	0.46	0.443	0.180	0.675
ImDiffusion	0.78	0.641	0.293	0.812	0.67	0.568	0.252	0.788
DiffTAD	0.88	0.681	0.298	0.902	0.82	0.736	0.263	0.803
DSTGRU	0.90	0.723	0.348	0.913	0.89	0.848	0.299	0.827

模型	TrackRisk				HighD
模型	Pre@100（↑）	Pre@300（↑）	Avg Pre（↑）	AUROC（↑）	Pre@100（↑）	Pre@300（↑）	Pre@100（↑）	AUROC（↑）
CVM	0.70	0.625	0.668	0.708	0.61	0.612	0.648	0.695
LTI	0.85	0.837	0.695	0.722	0.75	0.695	0.628	0.677
TOP-EYE	0.87	0.797	0.721	0.735	0.76	0.781	0.659	0.688
Seq2Seq	0.86	0.868	0.738	0.741	0.77	0.815	0.688	0.719
STGAE	0.82	0.752	0.655	0.672	0.72	0.703	0.636	0.608
ImDiffusion	0.84	0.767	0.738	0.812	0.82	0.814	0.754	0.792
DiffTAD	0.94	0.925	0.849	0.898	0.84	0.862	0.773	0.844
DSTGRU	0.95	0.935	0.856	0.905	0.87	0.883	0.797	0.885

Vehicle trajectory anomaly detection based on multi-level spatio-temporal interaction dependency

基于多层次时空交互依赖的车辆轨迹异常检测

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Figures/Tables 14

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Metrics

模型	Slow（慢速）			Stalled（停滞）			Tailgating（跟车）
模型	Pre@100	Avg Pre	AUROC	Pre@100	Avg Pre	AUROC	Pre@100	Avg Pre	AUROC
CVM	0.02	0.025	0.418	0.00	0.028	0.015	0.05	0.165	0.832
LTI	0.00	0.020	0.430	0.00	0.027	0.014	0.03	0.155	0.820
TOP-EYE	0.02	0.029	0.564	0.00	0.359	0.455	0.02	0.140	0.541
Seq2Seq	0.03	0.041	0.655	0.04	0.051	0.662	0.16	0.085	0.698
STGAE	0.00	0.045	0.560	0.00	0.022	0.440	0.02	0.050	0.480
ImDiffusion	0.19	0.116	0.812	0.08	0.178	0.622	0.32	0.225	0.763
DiffTAD	0.23	0.157	0.864	0.11	0.207	0.705	0.59	0.383	0.854
DSTGRU	0.20	0.185	0.902	0.09	0.249	0.831	0.65	0.393	0.861

GAT	MSTIDG	BiGRU	RES	Pre@100	Avg Pre	AUROC
√	—	—	—	0.55	0.185	0.760
√	√	—	—	0.68	0.240	0.783
√	√	√	—	0.75	0.275	0.870
√	√	√	√	0.90	0.348	0.913

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