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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Physics-informed reinforcement learning-based car-following control model for autonomous vehicles

Zhou Ruixiang1a
Yang Da1b
Zhu Liling2
1. a. School of Computing & Artificial Intelligence, b. School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
2. School of Business, Sichuan Normal University, Chengdu 610100, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0473

Abstract

Car-following control is a fundamental technique for autonomous driving. In recent years, reinforcement learning has been widely adopted in car-following tasks, enabling models to exhibit strong learning and imitation capabilities. However, reinforcement learning-based models face challenges such as poor interpretability and unstable outputs, which pose potential safety risks. To address these issues, this paper proposes a Physics-Informed Reinforcement Learning Car-Following Model (PIRL-CF) . The model incorporates vehicle dynamics, defines continuous state and action spaces, and integrates three classical car-following models with reinforcement learning to enhance stability and interpretability. A simulation environment was constructed using Python and the SUMO traffic simulator to train the PIRL-CF model. Comparative experiments were conducted against traditional car-following models and mainstream deep reinforcement learning models (DDPG and TD3) . Experimental results show that the PIRL-CF model improves the proportion of comfort zones by 8% compared to deep reinforcement learning models. Additionally, it increases the minimum time-to-collision by 0.3 seconds and the average headway distance by 0.21 seconds compared to traditional models. These results demonstrate that the PIRL-CF model achieves a balance of safety, comfort, and driving efficiency in car-following tasks, providing an effective solution for autonomous driving decision-making.

Foundation Support

四川省自然科学基金资助项目(23NSFSC4315,24NSFSC1109)
国家自然科学基金资助项目(52172333)
中央高校基本业务经费(2682024ZTPY018)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0473
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

周瑞祥, 杨达, 祝俪菱. 基于物理信息强化学习的无人驾驶车辆跟驰控制模型 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0473. (Zhou Ruixiang, Yang Da, Zhu Liling. Physics-informed reinforcement learning-based car-following control model for autonomous vehicles [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0473. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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