Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 4.
5 papers.
- 2024AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Mergingcited by 22
- 2024Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Frameworkcited by 16
- 2023Car-Following Models: A Multidisciplinary Reviewcited by 8
- 2024Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehiclescited by 1
- 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenariocited by 0
- 2024Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles5 Dec 2024
- 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario30 Oct 2024
- 2024Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework19 Sep 2024
- 2024AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging7 Aug 2024
- 2023Car-Following Models: A Multidisciplinary Review14 Apr 2023