  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.
