In this study, we propose a rotation-based connected automated vehicle (CAV) distributed cooperative control strategy for an on-ramp merging scenario. By assuming the mainline and ramp line are straight, we firstly design a virtual rotation approach that transfers the merging problem to a virtual car following (CF) problem to reduce the complexity and dimension of the cooperative CAVs merging control. Based on this concept, a multiple-predecessor virtual CF model and a unidirectional multi-leader communication topology are developed to determine the longitudinal behavior of each CAV. Specifically, we exploit a distributed feedback and feedforward longitudinal controller in preparation for actively generating gaps for merging CAVs, reducing the voids caused by merging, and ensuring safety and traffic efficiency during the process. To ensure the disturbance attenuation property of this system, practical string stability is mathematically proved for the virtual CF controllers to prohibit the traffic oscillation amplification through the traffic stream. Moreover, as a provision for extending the virtual CF application scenarios of any curvy ramp geometry, we utilize a curvilinear coordinate to model the two-dimensional merging control, and further design a local lateral controller based on an extended linear-quadratic regulator to regulate the position deviation and angular deviation of the lane centerlines. For the purpose of systematically evaluating the control performance of the proposed methods, numerical simulation experiments are conducted. As the results indicate, the proposed controllers can actively reduce the void and meanwhile guarantee the damping of traffic oscillations in the merging control area.
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究所）；Virginia Tech Transportation Institute（弗吉尼亚理工大学交通研究所）
Fault-Tolerant MARL for CAVs under Observation Perturbations for Highway On-Ramp Merging
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（北京信息科学与技术国家研究中心（BNRist），清华大学）
V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Tsinghua University（清华大学）；Brunel University London（伦敦布鲁内尔大学）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；the Hong Kong University of Science and Technology (HKUST)（香港科技大学）
Analysis of lane-change conflict between cars and trucks at merging section using UAV video data
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）；China Railway Siyuan Survey and Design Group Co., Ltd.（中铁第四勘察设计院集团有限公司）；Institute of Logistics Science and Engineering, Shanghai Maritime University（上海海事大学物流科学与工程研究院）
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究院）；Virginia Tech Transportation Institute（弗吉尼亚理工交通研究所）
