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）模型是交通模拟的核心组件，并已集成到众多配备先进驾驶辅助系统（ADAS）的量产车辆中。对跟驰行为的研究有助于识别由车辆两两交互基本过程引发的各种宏观现象根源。跟驰行为与控制模型涵盖交通工程、物理学、认知科学、机器学习及强化学习等多个领域。本文提供全面综述，重点揭示基于不同底层逻辑与原理的各类跟驰模型之间的差异、互补性及重叠之处。我们回顾了代表性算法，涵盖基于理论的运动学模型、刺激-响应模型、巡航控制模型，以及基于数据的行为克隆（BC）与模仿学习（IL），并阐述了它们的优势与局限性。本综述将基于不同原理构建的跟驰模型进行分类，并以整体框架总结海量文献。