Learning Switching Models for Abnormality Detection for Autonomous Driving

We present an approach to learn a model to estimate the dynamical states at continuous and discrete inference levels when trajectory information is available. We learn from sparse data a probabilistic switching model that generates trajectories associated with a stationary plan of an agent. The learned generative model is used within a Markov Jump Linear System (MJLSs) to switch among set of space dependent linear filters that analyze new trajectories and detect deviations from the learned model based on internal innovation measurements. We show examples of application of the proposed approach to learn filters for evaluating deviations from a reference human driving task execution that includes static and dynamic obstacle avoidance.