Please note: This PhD defence will take place online.
Frédéric Bouchard, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Krzysztof Czarnecki
An increasing number of road users are travelling freely in urban environments. Each of them has their own motion preferences but are expected to comply with the traffic laws. To cope with the motion discrepancies, autonomous vehicles require highly sophisticated reactive decision-making that can adapt their motion given the surrounding environment and the applicable traffic laws. Such decision-makers must be trustworthy, since each mistake can lead to a fatality, and performant, since they must estimate, at a high frequency, which behaviour to implement.
This thesis describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions and a precise notion of requirements. We first demonstrate the feasibility of planning the motion of an autonomous vehicle by implementing a prototype that, given a curated training suite of driving examples, can create and maintain a two-layer rule-based theory. Assuming perfect perception, we then design a method that learns the rules based on a precise notion of requirements. An expert anticipates that the decision-maker can enter a state for which a requirement is unmet and therefore specifies with a set of template rules the cause of each anticipated violation. For each template rule, its antecedent entails a notion of causality, and its consequent specifies the behaviour to implement. The set of template rules are used as a labelling function. Namely, each time the decision-maker fails to satisfy a requirement, an associated template rule is used to address the misbehaviour. The rules of the rule-based theory are based on templates. The antecedent of such rules are automatically learned and may have been significantly altered to include new relevant constraints that are expected to cope better with the requirements. Finally, considering that autonomous vehicles rely on sensor capabilities, we thereafter extend our method to compete in the Carla Leaderboard operational design domain. Using the same computer vision as the best performer for which there is code available, we demonstrate that our system can learn a policy that is explainable while performing better than our competitor on the set of provided requirements.