A Multi-Vehicle Self-Organized Cooperative Control Strategy for Platoon Formation in Connected Environment

Aiming at the problem of low merging efficiency, poor platoon stability, and high collision risk when multiple connected and automated vehicles merge into the same target platoon, we propose a multi-vehicle self-organized cooperative control strategy for platoon formation, which includes vehicle self-organizing formation control and platoon cooperative merging control. The vehicle self-organizing formation control module organizes the merging vehicles within the V2V communication range into multiple local platoons. The dynamic self-adjusting critical interval and a fixed-topology second-order platoon consistency control protocol are proposed to divided the vehicles reasonably and make the states of local platoon vehicles consistent. The merging vehicles merge into the target platoon as a whole in the form of a local platoon, which transforms the complex multi-vehicle merge problem into a platoon cooperative control problem and improves the merging efficiency. The platoon cooperative merging control module adopts a distributed model predictive control (DMPC) theory to design two longitudinal cooperative merging controllers, which control the target platoon to split to create a merging gap and the local platoon to align with this gap longitudinally. The lateral merging controller controls the local platoon change the lane to merge into the target platoon safely and smoothly. Simulation experiments are conducted in typical scenarios, and it is verified that the proposed control strategy can enable multi-vehicles to merge into a platoon efficiently, safely, and stably.

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Institutions

Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-01-29
DOI
https://doi.org/10.1109/tits.2024.3504572
Citations
15
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
18.22

Funders

Controls
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article

A Multi-Vehicle Self-Organized Cooperative Control Strategy for Platoon Formation in Connected Environment

Wanzhong Zhao, Chunyan Wang, Ziyu Zhang, Mengqi Zhang et al.
15 citations
IEEE Transactions on Intelligent Transportation Systems
Traffic control and management
18.22
article

A Multi-Vehicle Self-Organized Cooperative Control Strategy for Platoon Formation in Connected Environment

Wanzhong Zhao, Chunyan Wang, Ziyu Zhang, Mengqi Zhang, Jinqiang Liu
article en
15 citations

Abstract

Aiming at the problem of low merging efficiency, poor platoon stability, and high collision risk when multiple connected and automated vehicles merge into the same target platoon, we propose a multi-vehicle self-organized cooperative control strategy for platoon formation, which includes vehicle self-organizing formation control and platoon cooperative merging control. The vehicle self-organizing formation control module organizes the merging vehicles within the V2V communication range into multiple local platoons. The dynamic self-adjusting critical interval and a fixed-topology second-order platoon consistency control protocol are proposed to divided the vehicles reasonably and make the states of local platoon vehicles consistent. The merging vehicles merge into the target platoon as a whole in the form of a local platoon, which transforms the complex multi-vehicle merge problem into a platoon cooperative control problem and improves the merging efficiency. The platoon cooperative merging control module adopts a distributed model predictive control (DMPC) theory to design two longitudinal cooperative merging controllers, which control the target platoon to split to create a merging gap and the local platoon to align with this gap longitudinally. The lateral merging controller controls the local platoon change the lane to merge into the target platoon safely and smoothly. Simulation experiments are conducted in typical scenarios, and it is verified that the proposed control strategy can enable multi-vehicles to merge into a platoon efficiently, safely, and stably.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(3)
Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 1%
Traffic control and management
18.22
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