IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v643y2024ics037843712400339x.html
   My bibliography  Save this article

Optimizing platoon safety through key node selection in pinning control strategy

Author

Listed:
  • Li, Linheng
  • Wang, Can
  • Gan, Jing
  • Zhao, Yan
  • Qu, Xu
  • Ran, Bin

Abstract

The advent of Connected and Automated Vehicles (CAVs) and vehicular platooning has ignited high expectations for improving the safety performance of future transportation systems. Present control strategies for CAV platoons primarily concentrate on regulating each vehicle within the platoon. Although theoretically equipped to optimize a variety of performance metrics, this method is heavily dependent on communication and computational resources, which may lead to exorbitant costs. This study proposes a pinning control strategy that focuses on the selection of pivotal nodes, thereby facilitating the optimization of safety indexes for the entire platoon through the control of a select group of vehicles. The strategy encompasses two critical aspects: the identification of key control nodes and the application of node-based control. Initially, a feature matrix that includes both the intrinsic dynamics of the vehicles and the dynamics relative to other vehicles is developed. Spectral clustering is then utilized to identify these pivotal nodes. Subsequently, specialized control mechanisms for these nodes are devised, based on Proportional-Integral-Derivative (PID) controllers, with the objective of improving platoon safety. The efficacy of this strategy is corroborated through numerical simulations. To assess the safety performance, four categories of Surrogate Safety Measures (SSMs), which consider distance and acceleration, are applied. The findings confirm that the pinning control strategy significantly enhances performance across various SSMs, considerably reducing the risk of collisions during platoon operations. Moreover, it improves operational efficiency and platoon stability, establishing its value as an effective method for safety enhancement.

Suggested Citation

  • Li, Linheng & Wang, Can & Gan, Jing & Zhao, Yan & Qu, Xu & Ran, Bin, 2024. "Optimizing platoon safety through key node selection in pinning control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    • Handle: RePEc:eee:phsmap:v:643:y:2024:i:c:s037843712400339x
    DOI: 10.1016/j.physa.2024.129830
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712400339X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129830?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Linjie & Ruan, Tiancheng & Ma, Ke & Dong, Changyin & Wang, Hao, 2021. "Impact of CAV platoon management on traffic flow considering degradation of control mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    2. Wang, Xiao Fan & Chen, Guanrong, 2002. "Pinning control of scale-free dynamical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 521-531.
    3. Cheng, Rongjun & Lyu, Hao & Zheng, Yaxing & Ge, Hongxia, 2022. "Modeling and stability analysis of cyberattack effects on heterogeneous intelligent traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    4. Gong, Siyuan & Du, Lili, 2018. "Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 25-61.
    5. Wang, Jian & Gong, Siyuan & Peeta, Srinivas & Lu, Lili, 2019. "A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 271-301.
    6. Liu, Zhongcheng & Sun, Dihua & Zhao, Min & Jin, Shuang & Zhang, Yicai, 2022. "Pinning control strategy and stability analysis of mixed platoon: A cyber–physical perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    7. Xiangdong Liu & Shengchao He & Pingli Lu & Haikuo Liu & Changkun Du, 2021. "Finite-time consensus for multi-agent systems with nonlinear dynamics under Euler digraph via pinning control," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(8), pages 1664-1674, June.
    8. Ma, Fangwu & Yang, Yu & Wang, Jiawei & Liu, Zhenze & Li, Jinhang & Nie, Jiahong & Shen, Yucheng & Wu, Liang, 2019. "Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication," Energy, Elsevier, vol. 189(C).
    9. Jiang, Rui & Hu, Mao-Bin & Zhang, H.M. & Gao, Zi-You & Jia, Bin & Wu, Qing-Song, 2015. "On some experimental features of car-following behavior and how to model them," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 338-354.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Hanyu & Du, Lili & Shen, Jinglai, 2022. "Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 104-142.
    2. Lou, Haoli & Lyu, Hao & Cheng, Rongjun, 2024. "A time-varying driving style oriented model predictive control for smoothing mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    3. Wang, Jian & Lu, Lili & Peeta, Srinivas, 2022. "Real-time deployable and robust cooperative control strategy for a platoon of connected and autonomous vehicles by factoring uncertain vehicle dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 88-118.
    4. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Li, Linheng & An, Bocheng & Wang, Zhiyu & Gan, Jing & Qu, Xu & Ran, Bin, 2024. "Stability analysis and numerical simulation of a car-following model considering safety potential field and V2X communication: A focus on influence weight of multiple vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    6. Chen, Shukai & Wang, Hua & Meng, Qiang, 2021. "Autonomous truck scheduling for container transshipment between two seaport terminals considering platooning and speed optimization," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 289-315.
    7. Wang, Jian & Zhou, Anye & Liu, Zhiyuan & Peeta, Srinivas, 2024. "Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    8. Wenle Zhang & Jianchang Liu, 2016. "Ultra-fast consensus of discrete-time multi-agent systems with multi-step predictive output feedback," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(6), pages 1465-1479, April.
    9. Miao, Qingying & Rong, Zhihai & Tang, Yang & Fang, Jianan, 2008. "Effects of degree correlation on the controllability of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6225-6230.
    10. Yunlong Wu & Qian Zhao & Hui Li, 2018. "Synchronization of directed complex networks with uncertainty and time-delay," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    11. Meng, Dongli & Song, Guohua & Huang, Jianchang & Lu, Hongyu & Wu, Yizheng & Yu, Lei, 2024. "Car-following model considering jerk-constrained acceleration stochastic process for emission estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    12. Chuanwei Zhang & Xibo Xue & Peilin Qin & Lingling Dong, 2023. "Research on a Speed Guidance Strategy for Mine Vehicles in Three-Fork Roadways Based on Vehicle–Road Coordination," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    13. Hou, Lin & Pei, Yulong & He, Qingling, 2023. "A car following model in the context of heterogeneous traffic flow involving multilane following behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    14. Xin-Wei Li & Hong-Zhi Miao, 2023. "How to Incorporate Autonomous Vehicles into the Carbon Neutrality Framework of China: Legal and Policy Perspectives," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    15. Yuan, Zijian & Wang, Tao & Zhang, Jing & Li, Shubin, 2022. "Influences of dynamic safe headway on car-following behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    16. Hatzenbühler, Jonas & Jenelius, Erik & Gidófalvi, Gyözö & Cats, Oded, 2023. "Modular vehicle routing for combined passenger and freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    17. Zhou, Yang & Ahn, Soyoung, 2019. "Robust local and string stability for a decentralized car following control strategy for connected automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 175-196.
    18. Pi, Dawei & Xue, Pengyu & Wang, Weihua & Xie, Boyuan & Wang, Hongliang & Wang, Xianhui & Yin, Guodong, 2023. "Automotive platoon energy-saving: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    19. Hossain, Md. Anowar & Tanimoto, Jun, 2022. "A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    20. Yi, Chengbo & Feng, Jianwen & Wang, Jingyi & Xu, Chen & Zhao, Yi, 2017. "Synchronization of delayed neural networks with hybrid coupling via partial mixed pinning impulsive control," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 78-90.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:643:y:2024:i:c:s037843712400339x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.