The car-following (CF) model is the core component for traffic simulations and has been built-in in many production vehicles with Advanced Driving Assistance Systems (ADAS). Research of CF behavior allows us to identify the sources of different macro phenomena induced by the basic process of pairwise vehicle interaction. The CF behavior and control model encompasses various fields, such as traffic engineering, physics, cognitive science, machine learning, and reinforcement learning. This paper provides a comprehensive survey highlighting differences, complementarities, and overlaps among various CF models according to their underlying logic and principles. We reviewed representative algorithms, ranging from the theory-based kinematic models, stimulus-response models, and cruise control models to data-driven Behavior Cloning (BC) and Imitation Learning (IL) and outlined their strengths and limitations. This review categorizes CF models that are conceptualized in varying principles and summarize the vast literature with a holistic framework.

翻译：车跟随（CF）模型是交通仿真的核心组成部分，并在许多生产车辆中配备了先进的驾驶辅助系统（ADAS）。CF行为的研究可以帮助我们确定由成对车辆交互的基本过程引起的不同宏观现象的来源。CF行为和控制模型涵盖了各种领域，如交通工程，物理学，认知科学，机器学习和强化学习。本文提供了一篇全面的综述，强调不同CF模型之间基于其基础逻辑和原理的差异，互补性和重叠性。我们审查了代表性算法，从基于理论的运动学模型，刺激反应模型和巡航控制模型到基于数据的行为克隆（BC）和模仿学习（IL），并概述了它们的优缺点。此综述将以综合性的框架将基于不同原则的CF模型进行分类，并总结广泛的文献。