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.

翻译：本文遵循物理引导建模方法，将神经微分方程网络融入车辆单轨模型的物理结构中。通过依赖单轨常微分方程的运动学关系，与纯基于物理的车辆单轨模型相比，仅需一个小型神经网络和少量训练样本即可显著提升模型精度。更具体而言，在所考虑场景中，平方误差之和降低了68%。此外，研究表明，物理引导的神经ODE模型的预测能力优于纯黑箱神经微分方程方法。