Vehicle Dynamic

Vehicle dynamics research focuses on accurately modeling and predicting a vehicle's behavior, primarily to enhance safety and performance in autonomous driving systems. Current research emphasizes hybrid approaches combining physics-based models with data-driven techniques like neural networks (including Physics-Informed Neural Networks and Transformers), Gaussian Processes, and meta-learning algorithms, often incorporating Kalman filtering for noise management. These advancements aim to improve the accuracy and robustness of vehicle models, particularly under challenging conditions like high speeds and varied terrains, leading to safer and more efficient autonomous vehicles and improved traffic simulation.