# Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory

arXiv: https://arxiv.org/abs/2003.11071 · HTML: https://mlsanity.com/paper/2003.11071/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation](https://mlsanity.com/paper/2503.15049/) (2025)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles](https://mlsanity.com/paper/2112.03232/) (2021)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [K-Level Policy Gradients for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.12117/) (2025)
- [Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes](https://mlsanity.com/paper/2201.01733/) (2022)
- [Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving](https://mlsanity.com/paper/2212.08800/) (2022)
- [CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving](https://mlsanity.com/paper/2504.02450/) (2025)

## Abstract (arXiv)

In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data.