  Human drivers can recognise fast abnormal driving situations to avoid
accidents. Similar to humans, automated vehicles are supposed to perform
anomaly detection. In this work, we propose the spatio-temporal graph
auto-encoder for learning normal driving behaviours. Our innovation is the
ability to jointly learn multiple trajectories of a dynamic number of agents.
To perform anomaly detection, we first estimate a density function of the
learned trajectory feature representation and then detect anomalies in
low-density regions. Due to the lack of multi-agent trajectory datasets for
anomaly detection in automated driving, we introduce our dataset using a
driving simulator for normal and abnormal manoeuvres. Our evaluations show that
our approach learns the relation between different agents and delivers
promising results compared to the related works. The code, simulation and the
dataset are publicly available on https://github.com/againerju/maad_highway.
