Vehicle Lateral Dynamics-Inspired Hybrid Model Using Neural Network for Parameter Identification and Error Characterization

Autonomous vehicle requires a high-precision lateral dynamics model for path following and lateral stability control. However, existing physical models suffer from low accuracy due to modeling simplification and inaccurate model parameters, while data-driven models lack physical interpretability and robustness. To address these issues, a hybrid architecture inspired by vehicle lateral dynamics is developed in this study, which embeds the data-driven model into a physical model for parameter identification and error characterization to achieve accurate and interpretable modeling. Specifically, a physical lateral dynamics model with error analysis is established at first, and the problems of modeling error characterization and parameter identification are formulated. Then, the physical lateral dynamics model is deformed, and the modeling errors and cornering stiffness are unified into compound parameters. Using this deformed physical model, the modeling errors can be characterized by the identification of these compound parameters. To obtain high-precision compound parameters, a neural network-based parameter identification method is proposed, and the identified time-varying parameters enable high-precision characterization of modeling errors and parameters using data knowledge. By embedding the neural network into the deformed physical model, a hybrid model integrating physical laws and data knowledge is finally established for the description of vehicle lateral dynamics. Simulation and experimental results demonstrate that the proposed hybrid model realizes more accurate modeling of vehicle lateral dynamics than conventional physical and data-driven models.

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