Autonomous racing is a critical research area for autonomous driving,
presenting significant challenges in vehicle dynamics modeling, such as
balancing model precision and computational efficiency at high speeds
(>280kmph), where minor errors in modeling have severe consequences. Existing
physics-based models for vehicle dynamics require elaborate testing setups and
tuning, which are hard to implement, time-intensive, and cost-prohibitive.
Conversely, purely data-driven approaches do not generalize well and cannot
adequately ensure physical constraints on predictions. This paper introduces
Deep Dynamics, a physics-informed neural network (PINN) for vehicle dynamics
modeling of an autonomous racecar. It combines physics coefficient estimation
and dynamical equations to accurately predict vehicle states at high speeds and
includes a unique Physics Guard layer to ensure internal coefficient estimates
remain within their nominal physical ranges. Open-loop and closed-loop
performance assessments, using a physics-based simulator and full-scale
autonomous Indy racecar data, highlight Deep Dynamics as a promising approach
for modeling racecar vehicle dynamics.

该研究论文介绍了一种用于模拟自动赛车车辆动力学的物理知识神经网络（PINN），它通过物理系数估计和动力学方程准确预测高速下的车辆状态，并包含了一个独特的物理约束层来确保内部系数估计保持在其标称物理范围内，相关实验表明该方法对于建模赛车车辆动力学具有良好的潜力。