Hybrid physics and neural network model for lateral vehicle dynamic state prediction

The physical modeling-based approaches tend to be over-simplistic and cannot forecast the complex dynamical phenomena, thus leading to non-negligible errors. It is not easy to measure some parameters precisely, and they are usually approximated roughly. However, this approximation reduces the modeling accuracy of the physical model, which is a common problem in complex systems research. It is well-known that neural networks are capable of encoding dynamic information. The vehicle can be accurately modeled by collecting data during its motion. However, purely data-driven approaches have low interpretability and cannot be used in commercial applications. In this work, we present a new hybrid modeling architecture. Based on the physical model, the deep learning method is introduced to expand the incomplete dynamics described by differential equations. Compared with the physical modeling-based and purely data-driven approaches, the proposed technique has lower modeling error and higher interpretability. We evaluate the performance of the hybrid model based on the collected data. The test results show that the proposed architecture successfully captures the vehicle dynamics and reduces the error caused by multi-step prediction compared to the data-driven models. The results also show that the proposed method has value for significant research and practical application.