Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

TL;DR

This paper introduces a stochastic inverse reinforcement learning approach to capture and replicate the diverse and complex driving behaviors of humans in realistic traffic scenarios, enhancing driver behavior modeling accuracy.

Contribution

A novel stochastic inverse reinforcement learning method is developed to learn a distribution of driver-specific cost functions from real driving data, capturing behavior richness.

Findings

The stochastic driver model better replicates human driving strategies than deterministic models.

The learned model effectively captures behavior diversity across different traffic scenarios.

Results demonstrate improved realism and variability in simulated driver behaviors.

Abstract

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.