Outracing Human Racers with Model-based Planning and Control for Time-trial Racing

TL;DR

This paper advances autonomous racing by developing a model-based planning and control framework that achieves near-human expert lap times in a high-fidelity simulator, addressing key design challenges.

Contribution

It introduces novel solutions for vehicle modeling, planning, and control to close the performance gap with human experts in autonomous racing.

Findings

Achieved top 0.95% lap time among human experts in GTS.

Validated the effectiveness of proposed modules through ablation studies.

Identified future directions for surpassing human performance.

Abstract

Autonomous racing has become a popular sub-topic of autonomous driving in recent years. The goal of autonomous racing research is to develop software to control the vehicle at its limit of handling and achieve human-level racing performance. In this work, we investigate how to approach human expert-level racing performance with model-based planning and control methods using the high-fidelity racing simulator Gran Turismo Sport (GTS). GTS enables a unique opportunity for autonomous racing research, as many recordings of racing from highly skilled human players can served as expert emonstrations. By comparing the performance of the autonomous racing software with human experts, we better understand the performance gap of existing software and explore new methodologies in a principled manner. In particular, we focus on the commonly adopted model-based racing framework, consisting of an offline trajectory planner and an online Model Predictive Control-based (MPC) tracking controller. We thoroughly investigate the design challenges from three perspective, namely vehicle model, planning algorithm, and controller design, and propose novel solutions to improve the baseline approach toward human expert-level performance. We showed that the proposed control framework can achieve top 0.95% lap time among human-expert players in GTS. Furthermore, we conducted comprehensive ablation studies to validate the necessity of proposed modules, and pointed out potential future directions to reach human-best performance.