Accurate dynamic modeling is critical for autonomous racing vehicles, especially during high-speed and agile maneuvers where precise motion prediction is essential for safety. Traditional parameter estimation methods face limitations such as reliance on initial guesses, labor-intensive fitting procedures, and complex testing setups. On the other hand, purely data-driven machine learning methods struggle to capture inherent physical constraints and typically require large datasets for optimal performance. To address these challenges, this paper introduces the Fine-Tuning Hybrid Dynamics (FTHD) method, which integrates supervised and unsupervised Physics-Informed Neural Networks (PINNs), combining physics-based modeling with data-driven techniques. FTHD fine-tunes a pre-trained Deep Dynamics Model (DDM) using a smaller training dataset, delivering superior performance compared to state-of-the-art methods such as the Deep Pacejka Model (DPM) and outperforming the original DDM. Furthermore, an Extended Kalman Filter (EKF) is embedded within FTHD (EKF-FTHD) to effectively manage noisy real-world data, ensuring accurate denoising while preserving the vehicle's essential physical characteristics. The proposed FTHD framework is validated through scaled simulations using the BayesRace Physics-based Simulator and full-scale real-world experiments from the Indy Autonomous Challenge. Results demonstrate that the hybrid approach significantly improves parameter estimation accuracy, even with reduced data, and outperforms existing models. EKF-FTHD enhances robustness by denoising real-world data while maintaining physical insights, representing a notable advancement in vehicle dynamics modeling for high-speed autonomous racing.

翻译：精确的动力学建模对于自动驾驶赛车至关重要，尤其是在高速和敏捷机动过程中，精确的运动预测对安全至关重要。传统的参数估计方法面临诸多限制，例如对初始猜测的依赖、劳动密集型的拟合过程以及复杂的测试设置。另一方面，纯数据驱动的机器学习方法难以捕捉固有的物理约束，并且通常需要大量数据集才能达到最佳性能。为应对这些挑战，本文提出了精细调优混合动力学方法，该方法整合了监督式和非监督式物理信息神经网络，将基于物理的建模与数据驱动技术相结合。FTHD使用较小的训练数据集对预训练的深度动力学模型进行精细调优，相比深度Pacejka模型等先进方法表现出更优的性能，并且超越了原始DDM。此外，扩展卡尔曼滤波器被嵌入到FTHD中，以有效处理噪声的现实世界数据，在保持车辆基本物理特性的同时确保精确的去噪。所提出的FTHD框架通过使用BayesRace基于物理的模拟器进行的缩放模拟以及来自Indy自动驾驶挑战赛的全尺寸现实世界实验进行了验证。结果表明，即使在数据减少的情况下，这种混合方法也显著提高了参数估计的准确性，并且优于现有模型。EKF-FTHD通过去噪现实世界数据同时保持物理洞察力来增强鲁棒性，代表了高速自动驾驶赛车车辆动力学建模领域的显著进步。