Residual Learning towards High-fidelity Vehicle Dynamics Modeling with Transformer

2025-02-17

AI Summary

High-precision vehicle dynamics modeling for autonomous driving remains challenging due to oversimplified physics-based models and poor generalizability of purely data-driven approaches. Method: This paper proposes a residual-learning correction paradigm—instead of directly regressing vehicle states, a deep network learns the prediction residuals of a physics-based model, thereby reducing modeling complexity and enhancing generalization. We introduce Transformer architecture to dynamics residual modeling for the first time, designing DyTR: a novel network that employs high-dimensional queries to implicitly represent residuals and iteratively fuses physical-state embeddings with data-driven features via cross-attention. Results: In simulation, DyTR reduces state prediction errors of a 3-DOF physics model by 92.3% (lateral) and 59.9% (yaw) on average, outperforming both the baseline physics model and state-of-the-art data-driven methods. The approach achieves effective synergy between physical priors and deep learning.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The vehicle dynamics model serves as a vital component of autonomous driving systems, as it describes the temporal changes in vehicle state. In a long period, researchers have made significant endeavors to accurately model vehicle dynamics. 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. Recent advancements in deep learning-based methods have addressed this limitation by directly regressing vehicle dynamics. However, the 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. The experiments in simulations demonstrate the proposed system works much better than physics model, and our proposed DyTR model achieves the best performances on dynamics state residual correction task, reducing the state prediction errors of a simple 3 DoF vehicle model by an average of 92.3% and 59.9% in two dataset, respectively.

Problem

• Enhance vehicle dynamics modeling accuracy
• Correct state residuals using deep learning
• Improve generalization with Transformer-based network

Innovation

• Transformer-based residual correction
• Deep neural networks state correction
• High-dimensional queries dynamics representation