Physics Embedded Neural Network Vehicle Model and Applications in Risk-Aware Autonomous Driving Using Latent Features

TL;DR

This paper introduces a physics-embedded neural network vehicle model that combines deep learning with physics knowledge, improving generalization and enabling risk-aware autonomous driving under uncertain conditions.

Contribution

It presents a novel integration of physics-based models with neural networks, enhancing vehicle dynamics modeling and control in autonomous driving applications.

Findings

Better generalization than vanilla neural networks

Latent features accurately represent tire forces

Outperforms baseline control in unknown friction scenarios

Abstract

Non-holonomic vehicle motion has been studied extensively using physics-based models. Common approaches when using these models interpret the wheel/ground interactions using a linear tire model and thus may not fully capture the nonlinear and complex dynamics under various environments. On the other hand, neural network models have been widely employed in this domain, demonstrating powerful function approximation capabilities. However, these black-box learning strategies completely abandon the existing knowledge of well-known physics. In this paper, we seamlessly combine deep learning with a fully differentiable physics model to endow the neural network with available prior knowledge. The proposed model shows better generalization performance than the vanilla neural network model by a large margin. We also show that the latent features of our model can accurately represent lateral tire forces without the need for any additional training. Lastly, We develop a risk-aware model predictive controller using proprioceptive information derived from the latent features. We validate our idea in two autonomous driving tasks under unknown friction, outperforming the baseline control framework.