Anomaly detection in vehicle trajectories is a critical task in autonomous driving and intelligent transportation systems. Existing methods primarily rely on rule-based approaches or shallow statistical models, which struggle to capture complex spatio-temporal patterns in modern traffic environments. Recent advances in deep learning have demonstrated strong potential in motion prediction, but their application to anomaly detection in vehicle trajectories remains underexplored.
This research aims to develop a deep learning-based trajectory anomaly detection framework, focusing on two types of anomalies. The first type arises from sensor errors, leading to abnormal trajectory data that do not reflect the actual vehicle behavior. The second type concerns driving anomalies caused by human errors, such as traffic violations or accidents. The proposed approach will leverage a motion prediction model as a foundation: when the predicted trajectory significantly deviates from the observed trajectory, an anomaly is reported. Furthermore, given the increasing demand for data privacy and the diversity of data sources across different regions and platforms, this research will incorporate a federated learning framework. Federated learning allows collaborative model training across multiple datasets from different organizations or vehicle fleets without sharing raw data. This ensures data privacy while enabling the detection model to generalize to various environments and sensor configurations.
