# Generalized dynamic cognitive hierarchy models for strategic driving behavior

arXiv: https://arxiv.org/abs/2109.09861 · HTML: https://mlsanity.com/paper/2109.09861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers](https://mlsanity.com/paper/2008.07303/) (2020)
- [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018)
- [Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles](https://mlsanity.com/paper/1904.10158/) (2019)
- [Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving](https://mlsanity.com/paper/2009.10033/) (2020)
- [A Formal Separation Between Strategic and Nonstrategic Behavior](https://mlsanity.com/paper/1812.11571/) (2018)

## Built on by (most cited first, 3 of 3)

- [Revealed Multi-Objective Utility Aggregation in Human Driving](https://mlsanity.com/paper/2303.07435/) (2023)
- [Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI](https://mlsanity.com/paper/2405.07773/) (2024)
- [Planning Autonomous Vehicle Maneuvering in Work Zones Through Game-Theoretic Trajectory Generation](https://mlsanity.com/paper/2603.19556/) (2026)

## Abstract (arXiv)

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.