Car Following Model

Car-following models aim to mathematically represent 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 models that accurately capture diverse human driving styles and adapt to dynamic traffic conditions, employing techniques like deep reinforcement learning, deep symbolic regression, and hybrid approaches combining physics-based models (e.g., Intelligent Driver Model) with data-driven methods (e.g., neural networks, transformers). These advancements are significant for improving traffic simulation accuracy, enhancing the safety and efficiency of autonomous vehicles, and optimizing advanced driver-assistance systems.