Car-Following Models for Human-Driven Vehicles and Autonomous Vehicles: A Systematic Review
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 8
Abstract
The focus of car-following models is to analyze the microscopic characteristics of traffic flows, with particular attention given to the interaction between adjacent vehicles. This paper presents a systematic review of existing studies on car-following models, with an emphasis on the behavior of both human-driven vehicles (HDVs) and autonomous vehicles (AVs). We classify car-following models based on their applicable background and structure. By considering driving behavior and specific model parameters, we identify the advantages and limitations of each microscopic simulation model in terms of accuracy and continuity. We also discuss model calibration methods, stability analysis, and the impact of complex traffic environments on the car-following process. Finally, we present detailed discussions of each model’s features and provide recommendations based on reviewed works and development trends for future research, including mixed traffic flow composed of AVs and HDVs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was supported by the Key Project (No. 52131203) and Youth Program (No. 52102375) of the National Natural Science Foundation of China, the Youth Program (No. BK20210247) of the Natural Science Foundation of Jiangsu Province, China, and the Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110260). Ziyuan Gu acknowledges the support from the High-Level Personnel Project of Jiangsu Province, China. Qixiu Cheng acknowledges the support from the Basic and Applied Basic Research Foundation of Guangdong Province, China.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 8 • August 2023
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© 2023 American Society of Civil Engineers.
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Received: Dec 11, 2022
Accepted: Mar 13, 2023
Published online: Jun 7, 2023
Published in print: Aug 1, 2023
Discussion open until: Nov 7, 2023
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