Probabilistic Modelling of Sensors in Autonomous Vehicles Autoregressive Input/Output Hidden Markov Models for Time Series Analysis

Abstract

Testing the quality of sensors in autonomous vehicles is crucial for safety verification. This is usually done by collecting a lot of data in many different settings. However, this can be very time consuming and expensive. Therefore, one is interested in virtual verification methods that simulate these situations, so many scenarios can be tested in parallel without actual hazards. In this thesis a generative model is created for the longitudinal errors in the sensors and an extension to the hidden Markov model, called autoregressive input/output hidden Markov model (AIOHMM) is implemented. In this extension the transition probabilities are conditioned on an input vector and the emissions are conditioned with the emissions at previous time steps, making it better suited for modelling long-term dependencies. We show that conditioning on the previous error is not enough to capture the behaviour of the errors, and that conditioning the transitions on an input is an important aspect of the model.