Problem
Research questions and friction points this paper is trying to address.
Review car-following models in traffic simulations
Compare microscopic traffic flow and control models
Analyze applications and trends in car-following algorithms
Car-Following Models: A Multidisciplinary Review
🤖 AI Summary
This paper addresses the lack of systematic evaluation of car-following (CF) models in traffic simulation and advanced driver-assistance systems (ADAS). Methodologically, it conducts the first interdisciplinary review integrating perspectives from transportation engineering, control theory, cognitive science, and machine learning—unifying the evolutionary trajectory from classical models (e.g., IDM, OVM) and psychophysical models to adaptive cruise control (ACC) algorithms and data-driven approaches (e.g., reinforcement learning, imitation learning). A multidimensional evaluation framework is proposed, assessing interpretability, generalizability, real-time performance, and deployability. The key contributions include identifying complementary strengths and critical knowledge gaps across modeling paradigms, delineating precise applicability boundaries for each model class, and proposing a future research framework for co-optimization of ADAS and traffic simulation. This work provides both theoretical foundations and practical guidelines for intelligent driving modeling.
📝 Abstract
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 us 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, 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 data-driven algorithms like Reinforcement Learning (RL) and Imitation Learning (IL). The manuscript discusses the strengths and limitations of these models and explores their applications in different contexts. This review synthesizes existing researches 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates multidisciplinary car-following models
Surveys theory-based and data-driven algorithms
Explores reinforcement and imitation learning applications
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