Dimitar Filev: Practical Approaches to Driving Policy Design for Autonomous Vehicles

The presentation deals with some practical facets of application of AI methods to designing driving policy for autonomous vehicles. Relationship between the reinforcement learning (RL) based solutions and the use of rule-based and model-based techniques for improving their robustness and safety are discussed. One approach to obtaining explainable RL models by learning alternative rule-based representations is proposed. The presentation also elaborates on the opportunities for extending the AI driving policy approaches by applying game theory inspired methodology to addressing diverse and unforeseen scenarios, and representing the negotiation aspects of decision making in autonomous driving.