Multi-vehicle Cooperative Merging Control Strategy for Expressway under New Mixed Traffic Environment

被引:0
|
作者
Chen, Ying [1 ]
E, Wenjuan [1 ]
Wang, Xiang [1 ]
Wan, Qixing [1 ]
Wang, Cheng [1 ]
Yang, Na [1 ]
机构
[1] Soochow Univ, Sch Rail Transportat, Suzhou, Peoples R China
关键词
CVIS; mixed traffic; ramp merging; cooperative control; dedicated CAVs lane; AUTOMATED VEHICLES; CONTROL FRAMEWORK; PHASE; FLOW;
D O I
10.1109/ICITE56321.2022.10101440
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the in-depth application of cooperative vehicle infrastructure system (CVIS) and automatic driving technology, a new traffic situation of hybrid driving of human driving vehicles (HDVs) and connected and automated Vehicles (CAVs) will certainly appear on the road in the future. Facing the problem of multi-vehicles confluence in the merging area of the expressway under the new mixed traffic environment, a cooperative merging control strategy is proposed to optimize the driving trajectories for the CAVs and reduce the traffic congestion in the merging area. First, on the basis of setting a dedicated CAVs lane on the mainline, different merging scenes are divided according to the traffic situations that the CAVs may encounter. Then, the vehicle cooperative merging model in the merging area is constructed to control the speed of CAVs. Finally, the model is analyzed and verified by simulation experiments. The simulation results show that the multi-vehicle cooperative merging control strategy proposed in this paper can make the speed distribution of the vehicles more uniform in the process of driving, and effectively improve passenger comfort and traffic efficiency in the merging area. At the same time, when the CAVs permeability is 50%, the total travel time in the system is also increased by about 8.09%.
引用
收藏
页码:603 / 608
页数:6
相关论文
共 50 条
  • [41] Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
    Shi, Haotian
    Zhou, Yang
    Wu, Keshu
    Wang, Xin
    Lin, Yangxin
    Ran, Bin
    Transportation Research Part C: Emerging Technologies, 2021, 133
  • [42] Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment
    Wang R.-M.
    Zhang X.-R.
    Zhao X.-M.
    Wu X.
    Fan H.-J.
    Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering, 2022, 22 (03): : 139 - 151
  • [43] Comparison of Cooperative Control Strategies for Freeway Merging Area under Mixed Traffic Flow with Differential Objectives
    Wang, Xi
    Wang, Xiang
    Shen, Jia-Yan
    Fu, Yu
    CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION, 2023, : 985 - 996
  • [44] Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
    Shi, Haotian
    Zhou, Yang
    Wu, Keshu
    Wang, Xin
    Lin, Yangxin
    Ran, Bin
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 133
  • [45] Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging
    Liu, Haoji
    Jahedinia, Fatemeh
    Mu, Zeyu
    Park, B. Brian
    2024 35TH IEEE INTELLIGENT VEHICLES SYMPOSIUM, IEEE IV 2024, 2024, : 1222 - 1229
  • [46] Stability Analysis and Speed-Coordinated Control of Mixed Traffic Flow in Expressway Merging Area
    Hao, Wei
    Rong, Donglei
    Zhang, Zhaolei
    Byon, Young-Ji
    Lv, Nengchao
    Chen, Ying
    JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS, 2022, 148 (11)
  • [47] Cooperative Target-capturing Strategy for Multi-vehicle Systems with Dynamic Network Topology
    Kawakami, Hiroki
    Namerikawa, Toru
    2009 AMERICAN CONTROL CONFERENCE, VOLS 1-9, 2009, : 635 - 640
  • [48] Multi-Vehicle Cooperative Decision-Making in Merging Area Based on Deep Multi-Agent Reinforcement Learning
    Gan, Quan
    Li, Bin
    Xiong, Zhengang
    Li, Zhenhua
    Liu, Yanyue
    SUSTAINABILITY, 2024, 16 (22)
  • [49] A Multi-Vehicle Cooperative Localization Method Based on Belief Propagation in Satellite Denied Environment
    Wang J.
    Wang L.
    Journal of Beijing Institute of Technology (English Edition), 2022, 31 (05): : 464 - 472
  • [50] Multi-vehicle flocking: Scalability of cooperative control algorithms using pairwise potentials
    Chuang, Yao-Li
    Huang, Yuan R.
    D'Orsogna, Maria R.
    Bertozzi, Andrea L.
    PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10, 2007, : 2292 - +