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With autonomous vehicles (AV) set to integrate further into regular humantraffic, there is an increasing consensus of treating AV motion planning as amulti-agent problem. However, the traditional game theoretic assumption ofcomplete rationality is too strong for the purpose of human driving, and thereis a need for understanding human driving as a \emph{bounded rational} activitythrough a behavioral game theoretic lens. To that end, we adapt threemetamodels of bounded rational behavior; two based on Quantal level-k and onebased on Nash equilibrium with quantal errors. We formalize the differentsolution concepts that can be applied in the context of hierarchical games, aframework used in multi-agent motion planning, for the purpose of creating gametheoretic models of driving behavior. Furthermore, based on a contributeddataset of human driving at a busy urban intersection with a total of ~4kagents and ~44k decision points, we evaluate the behavior models on the basisof model fit to naturalistic data, as well as their predictive capacity. Ourresults suggest that among the behavior models evaluated, modeling drivingbehavior as pure strategy NE with quantal errors at the level of maneuvers withbounds sampling of actions at the level of trajectories provides the best fitto naturalistic driving behavior, and there is a significant impact ofsituational factors on the performance of behavior models.

Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving