# A Probabilistic Framework for Imitating Human Race Driver Behavior

arXiv: https://arxiv.org/abs/2001.08255 · HTML: https://mlsanity.com/paper/2001.08255/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

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

- [An Adaptive Human Driver Model for Realistic Race Car Simulations](https://mlsanity.com/paper/2203.01909/) (2022)
- [Race Driver Evaluation at a Driving Simulator using a physical Model and a Machine Learning Approach](https://mlsanity.com/paper/2201.12939/) (2022)
- [MSACL: Multi-Step Actor-Critic Learning with Lyapunov Certificates for Exponentially Stabilizing Control](https://mlsanity.com/paper/2512.24955/) (2025)

## Abstract (arXiv)

Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and the corresponding choice of actions is performed by a neural network. Experiments in a simulated car racing setting show considerable advantages in imitation accuracy and robustness compared to other imitation learning algorithms. The modular architecture of the proposed framework facilitates straightforward extensibility in driving line adaptation and sequencing of multiple movement primitives for future research.