Generalized dynamic cognitive hierarchy models for strategic driving behavior

TL;DR

This paper introduces a generalized dynamic cognitive hierarchy framework for modeling human driving and planning autonomous vehicle behavior, addressing common knowledge and bounded rationality challenges with empirical validation.

Contribution

It develops a novel framework combining automata strategies and satisficing notions to improve game theoretic modeling and planning in autonomous driving.

Findings

Automata strategies effectively model level-0 driving behavior.

The robust response approach improves planning in heterogeneous traffic scenarios.

Empirical results validate the framework on naturalistic datasets.

Abstract

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.