Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based autonomous test driver capable of evaluating vehicle setups on racetracks, aiming to match human expert performance and enable driver-specific vehicle optimization.

Contribution

It presents the first autonomous driver model trained with reinforcement learning that can assess vehicle configurations and incorporate imitation learning for human-like driving behavior.

Findings

Autonomous driver achieves human-level racing performance.

Model can be tuned for driver-specific behavior.

Framework enables efficient vehicle setup evaluation.

Abstract

Success in racing requires a unique combination of vehicle setup, understanding of the racetrack, and human expertise. Since building and testing many different vehicle configurations in the real world is prohibitively expensive, high-fidelity simulation is a critical part of racecar development. However, testing different vehicle configurations still requires expert human input in order to evaluate their performance on different racetracks. In this work, we present the first steps towards an autonomous test driver, trained using deep reinforcement learning, capable of evaluating changes in vehicle setup on racing performance while driving at the level of the best human drivers. In addition, the autonomous driver model can be tuned to exhibit more human-like behavioral patterns by incorporating imitation learning into the RL training process. This extension permits the possibility of driver-specific vehicle setup optimization.