Interpretable Anomaly Detection Using A Generalized Markov Jump Particle Filter

When performing anomaly detection on an autonomous vehicle’s sensory data, it is fundamental to infer the cause of the found anomalies. This paper proposes a method for learning prediction models and detecting anomalies by decomposing the evolution of an agent’s state into its different motion-related parameters. A filter is introduced based on Generalized Filtering to increase the interpretabilit...