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.

翻译：自主赛车是自动驾驶的关键研究领域，在车辆动力学建模方面面临重大挑战，例如在高速（>280公里/小时）条件下平衡模型精度与计算效率——此时建模中的微小误差将导致严重后果。现有基于物理的车辆动力学模型需要复杂的测试设备与调参，难以实施、耗时长且成本高昂。而纯数据驱动方法泛化能力不足，无法充分确保预测结果满足物理约束。本文提出深度动力学（Deep Dynamics），一种用于自主赛车车辆动力学建模的物理信息神经网络（PINN）。该方法融合物理系数估计与动力学方程，可在高速状态下精确预测车辆状态，并创新性地引入物理防护层（Physics Guard）以确保内部系数估计值始终处于标称物理范围内。基于物理仿真器与全尺寸自主印地赛车数据的开环与闭环性能评估表明，深度动力学是建模赛车车辆动力学的有效方法。