Spectral Clustering-based Pinning Control Strategy for Vehicle Platoons in Connected and Automated Environments

被引：0
|
作者：
Wang, Can [1 ,2 ,3 ]
Zhao, Yan [1 ,2 ,3 ]
Li, Lin-Heng [1 ,2 ,3 ]
Qu, Xu [1 ,2 ,3 ]
Ran, Bin [1 ,2 ,3 ]
机构：
[1] School of Transportation, Southeast University, Jiangsu, Nanjing
[2] Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Jiangsu, Nanjing
[3] Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Jiangsu, Nanjing
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2024年 / 37卷 / 11期
基金：
中国国家自然科学基金;
关键词：
automated environment; connected; mixed traffic optimization; pinning control; spectral clustering; traffic engineering; vehicle platoon;
D O I：
10.19721/j.cnki.1001-7372.2024.11.018
中图分类号：
学科分类号：
摘要：
In connected and automated environments, implementing feedback control on key connected and automated vehicles in a vehicle platoon can indirectly influence the operation of human-driven vehicles to thereby optimize the overall traffic flow in terms of efficiency and safety. Hence, this study proposes a spectral clustering-based pinning control (SC-PC) strategy to optimize the microcontrol effects of vehicle platoons in a connected and automated environment and enhance the overall traffic flow performance. First, a network model and definition of pinning control is proposed for vehicle platoons in a connected and automated environment. Second, by comprehensively considering the static network topology information and the dynamic information of vehicle dynamics, a key control node identification method based on the spectral clustering algorithm is proposed to determine the implementation objects of pinning control. Subsequently, a feedback control method targeting pinning node vehicles is designed, guided by multiple objectives, such as safety and efficiency. Finally, numerical simulation experiments are conducted using the TOD dataset of vehicle following behavior in real scenarios. The effects of different pinning node identification methods and pinning rates on the pinning control were compared and analyzed, and the effectiveness of the proposed SC-PC strategy was verified. The results show that, compared with other pinning control strategies, the proposed SC-PC strategy can more accurately identify key control nodes within the vehicle platoon to improve traffic oscillation, synchronization, and safety indicators by at least 5.3%, 11.7%, and 16.0%, respectively. Thus, the proposed method can enhance the anti-interference ability of traffic flow while simultaneously optimizing efficiency and safety to serve as a balance between resource input and control effects in traffic flow optimal-control issues in connected and automated environments. © 2024 Chang'an University. All rights reserved.
引用
收藏
页码：235 / 248
页数：13
相关论文
共 41 条
  • [1] HONG Jin-long, GAO Bing-zhao, DONG Shi-ying, Et al., Key problems and research progress of energy saving optimization for intelligent connected vehicles [J], China Journal of Highway and Transport, 34, 11, pp. 306-334, (2021)
  • [2] HU Yun-feng, LIU Di, ZHAO Jing-hua, Et al., Research status and prospect of vehicle energy consumption and emission optimization control in intelligent network environment [J], China Journal of Highway and Transport, 35, 3, pp. 1-14, (2022)
  • [3] LI J J, YU C H, SHEN Z L, Et al., A survey on urban traffic control under mixed traffic environment with connected automated vehicles, Transportation Research Part C: Emerging Technologies, 154, (2023)
  • [4] LI L H, WANG C, ZHANG Y, Et al., Microscopic state evolution model of mixed traffic flow based on potential field theory, Physica A: Statistical Mechanics and Its Applications, 607, (2022)
  • [5] ZHOU J Z, ZHU F., Modeling the fundamental diagram of mixed human-driven and connected automated vehicles, Transportation Research Part C: Emerging Technologies, 115, (2020)
  • [6] CHEN Zheng, ZHANG Yu-guo, SHEN Shi-quan, Et al., Speed planning of intelligent and connected vehicle under following conditions of suburban road scenarios [J], China Journal of Highway and Transport, 36, 6, pp. 298-310, (2023)
  • [7] JING S C, ZHAO X M, HUI F, Et al., Cooperative CAVs optimal trajectory planning for collision avoidance and merging in the weaving section [J], Transportmetrica B: Transport Dynamics, 9, 1, pp. 219-236, (2021)
  • [8] CHEN D, HAJIDAVALLOO M R, LI Z J, Et al., Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic [J], IEEE Transactions on Intelligent Transportation Systems, 24, 11, pp. 11623-11638, (2023)
  • [9] WANG S P, WANG Z Y, JIANG R, Et al., A multi-agent reinforcement learning-based longitudinal and lateral control of CAVs to improve traffic efficiency in a mandatory lane change scenario, Transportation Research Part C: Emerging Technologies, 158, (2024)
  • [10] SHI H T, CHEN D J, ZHENG N, Et al., A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon, Transportation Research Part C: Emerging Technologies, 148, (2023)
12345