Anomaly Detection in Multi-Agent Trajectories for Automated DrivingDownload PDF

19 Jun 2021, 10:04 (edited 04 Nov 2021)CoRL2021 PosterReaders: Everyone
  • Keywords: Anomaly Detection, Multi-Agent Trajectory, Graph Neural Networks, Automated Driving
  • Abstract: 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.
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  • Poster: jpg
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