Vehicle single track modeling using physics guided neural differential equations

TL;DR

This paper introduces a physics-guided neural differential equation model for vehicle single track dynamics, significantly improving accuracy with minimal training data by integrating physical principles into neural networks.

Contribution

It presents a novel approach combining physics-based modeling with neural differential equations, enhancing vehicle dynamics prediction accuracy over traditional models.

Findings

68% reduction in sum of squared error

Superior prediction accuracy compared to pure black box neural ODEs

Efficient modeling requiring few training samples

Abstract

In this paper, we follow the physics guided modeling approach and integrate a neural differential equation network into the physical structure of a vehicle single track model. By relying on the kinematic relations of the single track ordinary differential equations (ODE), a small neural network and few training samples are sufficient to substantially improve the model accuracy compared with a pure physics based vehicle single track model. To be more precise, the sum of squared error is reduced by 68% in the considered scenario. In addition, it is demonstrated that the prediction capabilities of the physics guided neural ODE model are superior compared with a pure black box neural differential equation approach.