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arXiv · 2403.11648

Vehicle single track modeling using physics guided neural differential equations

Stephan Rhode·Fabian Jarmolowitz·Felix Berkel
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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.

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Stephan Rhode, Fabian Jarmolowitz, Felix Berkel. 2024-03-18. Vehicle single track modeling using physics guided neural differential equations. https://arxiv.org/abs/2403.11648

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