Interpretable anomaly detection using a Generalized Markov Jump Particle Filter

When performing anomaly detection on sensory data of an autonomous vehicle, 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 the state of an agent into its different motion-related parameters. A filter is introduced, based on the concept of Generalized Filtering, with the objective of increasing the interpretability of the results with respect to previous methods. The proposed anomaly detection method is tested on data from a real vehicle. We also consider the case in which multiple models are learned, how to extract the salient discriminatory features of each, and use the proposed anomaly detection method to perform behavior classification.