# Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

arXiv: https://arxiv.org/abs/2107.06344 · HTML: https://mlsanity.com/paper/2107.06344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 5 of 5)

- [Efficient Model Learning for Human-Robot Collaborative Tasks](https://mlsanity.com/paper/1405.6341/) (2014)
- [Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2006.13704/) (2020)
- [Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving](https://mlsanity.com/paper/1905.00229/) (2019)
- [Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference](https://mlsanity.com/paper/2008.08812/) (2020)
- [Personalized Adaptive Cruise Control and Impacts on Mixed Traffic](https://mlsanity.com/paper/2103.14705/) (2021)

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

- [HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation](https://mlsanity.com/paper/2503.15049/) (2025)
- [Mathematical Models of Human Drivers Using Artificial Risk Fields](https://mlsanity.com/paper/2205.12722/) (2022)
- [Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study](https://mlsanity.com/paper/2608.06445/) (2026)

## Abstract (arXiv)

Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness and richness of human driver behavior in realistic driving scenarios. A stochastic inverse reinforcement learning (SIRL) approach is proposed to learn a distribution of cost function, which represents the richness of the human driver behavior with a given set of driver-specific demonstrations. Evaluations are conducted on the realistic driving data collected from the 3D driver-in-the-loop driving simulation. The results show that the learned stochastic driver model is capable of expressing the richness of the human driving strategies under different realistic driving scenarios. Compared to the deterministic baseline driver behavior model, the results reveal that the proposed stochastic driver behavior model can better replicate the driver's unique and rich driving strategies in a variety of traffic conditions.