System Identification for Dynamic Modeling of Large Steering Angle Vehicles

TL;DR

This paper develops and compares advanced vehicle models incorporating wide steering angles, demonstrating physics-informed neural networks outperform traditional models in accuracy and efficiency.

Contribution

It introduces modified bicycle models with combined parametric and non-parametric identification, including physics-informed neural networks for large steering angle vehicle modeling.

Findings

Physics-informed neural networks outperform physical baseline models in accuracy.

Models with physical knowledge balance accuracy and computational efficiency.

Systematic comparison highlights tradeoffs in model complexity and performance.

Abstract

This paper presents the modeling of autonomous vehicles with high maneuverability used in an experimental framework for educational purposes. Since standard bicycle models typically neglect wide steering angles, we develop modified planar bicycle models and combine them with both parametric and non-parametric identification techniques that progressively incorporate physical knowledge. The resulting models are systematically compared to evaluate the tradeoff between model accuracy and computational requirements, showing that physics-informed neural network models surpass the purely physical baseline in accuracy at lower computational cost.