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：跟驰（CF）模型是交通仿真的核心组成部分，并已集成于众多配备高级驾驶辅助系统（ADAS）的量产车辆中。对跟驰行为的研究使我们能够识别由两车基本交互过程引发的不同宏观现象的根源。跟驰行为与控制模型涵盖交通工程、物理学、认知科学、机器学习以及强化学习等多个领域。本文提供了一项全面综述，根据不同跟驰模型的内在逻辑与原理，重点阐述了它们之间的差异、互补性及其重叠之处。我们回顾了从基于理论的运动学模型、刺激-响应模型、巡航控制模型到数据驱动的行为克隆（BC）与模仿学习（IL）等代表性算法，并概述了各自的优势与局限性。本综述对基于不同原理构建的跟驰模型进行了分类，并借助一个整体框架对大量相关文献进行了归纳总结。