Generalized dynamic cognitive hierarchy models for strategic driving behavior
This addresses practical challenges in game-theoretic autonomous driving by improving behavior modeling and planning for AVs, though it appears incremental as it builds on existing cognitive hierarchy concepts.
The paper tackled the problem of modeling strategic driving behavior for autonomous vehicles by developing a generalized dynamic cognitive hierarchy framework, showing that automata strategies suit level-0 behavior and robust response is effective for planning, with evaluation on two large naturalistic datasets and simulations.
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.