Vehicle single track modeling using physics guided neural differential equations
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.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2024
- DOI:
- 10.48550/arXiv.2403.11648
- arXiv:
- arXiv:2403.11648
- Bibcode:
- 2024arXiv240311648R
- Keywords:
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- Computer Science - Computational Engineering;
- Finance;
- and Science
- E-Print:
- preprint, 11 pages