PhD Seminar • Artificial Intelligence • Generalized Dynamic Cognitive Hierarchy Models for Strategic Driving Behavior

Thursday, July 14, 2022 11:30 am - 12:30 pm EDT (GMT -04:00)

Please note: This PhD seminar will be given online.

Atrisha Sarkar, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Krzysztof Czarnecki

While there has been an increasing focus on the use of game theoretic models for autonomous driving, empirical evidence shows that there are still open questions around dealing with the challenges of common knowledge assumptions as well as modeling bounded rationality. In this seminar, I will address two key challenges of designing game theoretic models for traffic behaviour from observational data in the context of a dynamic game; model instability and model uncertainty. Model instability arises when a class of boundedly rational agents who follow elementary nonstrategic models of behaviour have no reason to stick to elementary models over time in the game. Model uncertainty arises when agents are free to follow any model of reasoning as is often the case in naturalistic data.

The result of this work is a framework of generalized dynamic cognitive hierarchy for both modelling naturalistic human driving behavior as well as behavior planning for autonomous vehicles (AV). This framework is built upon a rich model of level-0 behavior through the use of automata strategies, an interpretable notion of bounded rationality through safety and maneuver satisficing, and a robust response for planning. Based on evaluation on two large naturalistic datasets as well as simulation of critical traffic scenarios, we show that i) automata strategies are well suited for level-0 behavior in a dynamic level-k framework, and ii) the proposed robust response to a heterogeneous population of strategic and non-strategic reasoners can be an effective approach for game theoretic planning in AV.