# Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes

arXiv: https://arxiv.org/abs/2201.01733 · HTML: https://mlsanity.com/paper/2201.01733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 12 of 12)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Imitating Driver Behavior with Generative Adversarial Networks](https://mlsanity.com/paper/1701.06699/) (2017)
- [Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.02926/) (2018)
- [Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems](https://mlsanity.com/paper/1608.08589/) (2016)
- [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018)
- [Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections](https://mlsanity.com/paper/1904.05423/) (2019)
- [Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field](https://mlsanity.com/paper/1906.10307/) (2019)
- [Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory](https://mlsanity.com/paper/2003.11071/) (2020)
- [Multi-Fidelity Recursive Behavior Prediction](https://mlsanity.com/paper/1901.01831/) (2018)
- [A 3D Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts](https://mlsanity.com/paper/1802.07218/) (2018)
- [Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory](https://mlsanity.com/paper/1910.05092/) (2019)

## Abstract (arXiv)

This paper proposes a method for modeling human driver interactions that relies on multi-output gaussian processes. The proposed method is developed as a refinement of the game theoretical hierarchical reasoning approach called "level-k reasoning" which conventionally assigns discrete levels of behaviors to agents. Although it is shown to be an effective modeling tool, the level-k reasoning approach may pose undesired constraints for predicting human decision making due to a limited number (usually 2 or 3) of driver policies it extracts. The proposed approach is put forward to fill this gap in the literature by introducing a continuous domain framework that enables an infinite policy space. By using the approach presented in this paper, more accurate driver models can be obtained, which can then be employed for creating high fidelity simulation platforms for the validation of autonomous vehicle control algorithms. The proposed method is validated on a real traffic dataset and compared with the conventional level-k approach to demonstrate its contributions and implications.