  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.
