Détail du document
Identifiant
oai:arXiv.org:2403.11648
Sujet
Computer Science - Computational E...Auteur
Rhode, Stephan Jarmolowitz, Fabian Berkel, FelixCatégorie
Computer Science
Année
2024
Date de référencement
20/03/2024
Mots clés
guided vehicle differential modelRésumé
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.
;Comment: preprint, 11 pages
Rhode, Stephan,Jarmolowitz, Fabian,Berkel, Felix, 2024, Vehicle single track modeling using physics guided neural differential equations
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