# A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior

arXiv: https://arxiv.org/abs/2006.08832 · HTML: https://mlsanity.com/paper/2006.08832/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Multi-Agent Variational Occlusion Inference Using People as Sensors](https://mlsanity.com/paper/2109.02173/) (2021)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay](https://mlsanity.com/paper/2402.14194/) (2024)
- [Cooperation for Scalable Supervision of Autonomy in Mixed Traffic](https://mlsanity.com/paper/2112.07569/) (2021)
- [PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems](https://mlsanity.com/paper/2308.00864/) (2023)
- [Cooperative Advisory Residual Policies for Congestion Mitigation](https://mlsanity.com/paper/2407.00553/) (2024)

## Abstract (arXiv)

An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation, and causal inference for generating explanations for autonomous driving. Modeling human driving behavior is challenging because it is stochastic, high-dimensional, and involves interaction between multiple agents. This problem has been studied in various communities with a vast body of literature. Existing reviews have generally focused on one aspect: motion prediction. In this article, we present a unification of the literature that covers intent estimation, trait estimation, and motion prediction. This unification is enabled by modeling multi-agent driving as a partially observable stochastic game, which allows us to cast driver modeling tasks as inference problems. We classify driver models into a taxonomy based on the specific tasks they address and the key attributes of their approach. Finally, we identify open research opportunities in the field of driver modeling.