  Predicting the future trajectories of surrounding vehicles based on their
history trajectories is a critical task in autonomous driving. However, when
small crafted perturbations are introduced to those history trajectories, the
resulting anomalous (or adversarial) trajectories can significantly mislead the
future trajectory prediction module of the ego vehicle, which may result in
unsafe planning and even fatal accidents. Therefore, it is of great importance
to detect such anomalous trajectories of the surrounding vehicles for system
safety, but few works have addressed this issue. In this work, we propose two
novel methods for learning effective and efficient representations for online
anomaly detection of vehicle trajectories. Different from general time-series
anomaly detection, anomalous vehicle trajectory detection deals with much
richer contexts on the road and fewer observable patterns on the anomalous
trajectories themselves. To address these challenges, our methods exploit
contrastive learning techniques and trajectory semantics to capture the
patterns underlying the driving scenarios for effective anomaly detection under
supervised and unsupervised settings, respectively. We conduct extensive
experiments to demonstrate that our supervised method based on contrastive
learning and unsupervised method based on reconstruction with semantic latent
space can significantly improve the performance of anomalous trajectory
detection in their corresponding settings over various baseline methods. We
also demonstrate our methods' generalization ability to detect unseen patterns
of anomalies.
