Residual Learning Towards High-Fidelity Vehicle Dynamics Modeling With Transformer

Abstract

The vehicle dynamics model serves as a vital component of autonomous driving systems, as it describes the temporal changes in vehicle state. Traditional physics-based methods employ mathematical formulae to model vehicle dynamics, but they are unable to adequately describe complex vehicle systems due to the simplifications they entail. The deep learning-based methods directly regress vehicle dynamics, but their performance and generalization capabilities still require further enhancement. In this letter, we address these problems by proposing a vehicle dynamics correction system that leverages deep neural networks to correct the state residuals of a physical model instead of directly estimating the states. This system greatly reduces the difficulty of network learning and thus improves the estimation accuracy of vehicle dynamics. Furthermore, we have developed a novel Transformer-based dynamics residual correction network, DyTR. This network implicitly represents state residuals as high-dimensional queries, and iteratively updates the estimated residuals by interacting with dynamics state features. Simulation experiments and real-world scaled-vehicle testing demonstrate the proposed vehicle dynamics correction system works much better than the physics-based vehicle model, and our proposed DyTR model achieves the state-of-the-art performance.