Car-Following Models: A Multidisciplinary Review

Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS). Insights from the model of car-following behavior help researchers to understand the causes of various macro phenomena that arise from interactions between pairs of vehicles. Car-following Models encompass multiple disciplines, including traffic engineering, physics, dynamic system control, cognitive science, machine learning, deep learning, and reinforcement learning. This paper presents an extensive survey that highlights the differences, complementarities, and overlaps among microscopic traffic flow and control models based on their underlying principles and design logic. It reviews representative algorithms, ranging from theory-based Kinematic Models, Psycho-Physical Models, and Adaptive Cruise Control Models to Learning-based algorithms like Reinforcement Learning (RL) and Imitation Learning (IL). To acknowledge the potential impact on CF models, Large GenAI Models are also included as Knowledge- Driven category. This manuscript discusses the strengths and limitations of these models and explores their applications in different contexts. This review synthesizes existing researches and available datasets across different domains to fill knowledge gaps and offer guidance for future research by identifying the latest trends in car following models and their applications.