Physics-Constrained Imitation Learning for Autonomous Racing

Abstract

Autonomous racing has become increasingly pop-ular in both academia and industry as a testbed for pushing general autonomous driving modules, such as perception, plan-ning, and control, to their limits. Although traditional control approaches can generate optimal control sequences at the edge of the racing vehicles’ physical controllability, they are highly sensitive to the accuracy of modeling parameters, such as tire model coefficients. Meanwhile, end-to-end learning methods are susceptible to distributional shifts, leading to unpredictable and irreversible failures. To address these challenges, this work introduces a physics-constrained imitation learning (PCIL) framework that effectively leverages the advantages of deep learning techniques and knowledge-driven strategies. Specifically, a fallback strategy would be automatically triggered when the vehicle states exceed predefined physical constraints. Meanwhile, the data from the knowledge-driven strategy will be augmented into the original dataset, and repeated re-training using an aggregated dataset could progressively improve PCIL. A series of simulations and real-world shadow testing are conducted at the Yas Marina circuit, and experimental results demonstrate superior performance compared to state-of-the-art methods, which suggests that it provides a promising solution for real-world autonomous racing.