  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. To address some of these practical
challenges, we develop 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.
