Causally-guided graph Mamba for detecting socially abnormal vehicle trajectories

With the rapid development of autonomous driving and internet of things (IoT) technologies, vehicle trajectory anomaly detection becomes one of the important tasks in road monitoring to help traffic data collection and intelligent traffic management. Most methods use simple vehicle interaction with static information to accomplish this task. In contrast, long sequences of trajectory spatio-temporal information can better describe the state of vehicle traveling. In this study, we introduce a trajectory anomaly detection pipeline for spatio-temporal graph modeling to address the challenges in this task. First, we propose a causally guided dynamic graph representation (CGDG) to efficiently model key interactions in complex trajectories. This bootstrapping and decoupling mechanism lays the foundation for our subsequent process. Subsequently, we parse the dynamic evolution process in the spatiotemporal graph by using spatio-tempora feature fusion Mamba (STFMamba) as an encoder and decoder. The anomaly results are generated by reconstruction probabilities. To address the challenge of scarce and expensive datasets in trajectory anomaly detection, we propose the TRAREAL benchmark dataset supplemented with various anomalous event scenarios for experiments. Our method performs well in the evaluation of natural trajectory benchmark datasets. The source codes are available at https://github.com/yongfengB/DATMamba.