Car Following

Car-following research focuses on modeling and predicting how vehicles maintain safe distances and react to each other's movements, a crucial aspect of traffic flow and autonomous driving. Current research emphasizes developing more accurate and adaptable car-following models using diverse techniques, including deep learning (e.g., recurrent neural networks, transformers), reinforcement learning, and Bayesian methods, often incorporating physical models like the Intelligent Driver Model (IDM) to enhance realism and interpretability. These advancements aim to improve the safety, efficiency, and fuel economy of both autonomous and human-driven vehicles, impacting traffic simulation, advanced driver-assistance systems (ADAS), and the design of safer and more efficient transportation systems. A key trend is the integration of human driving data and preferences to create more personalized and realistic models.