Modeling and analyzing V2V communication limitations impacts on connected and automated vehicle platoons

Connected and automated vehicle (CAV) platooning, enabled by Vehicle-to-Vehicle (V2V) communication, promises significant improvements in traffic safety, throughput, and energy efficiency. However, communication constraints - such as range limitations and intermittent connectivity - disrupt information flow, destabilizing platoon dynamics. Existing models lack a unified framework to analyze how these constraints propagate through CAV interactions. To address this gap, the Platoon Intelligent Driver Model (PIDM) is proposed, a novel analytical framework that integrates dynamic communication topologies (predecessor-following, predecessor-leader-following, and 𝕜-predecessor-leader-following) with vehicle dynamics under V2V limitations. The PIDM enables systematic stability analysis and quantifies disturbance propagation mechanisms. Through numerical simulations, the study demonstrates that: (1) 𝕜-predecessor-leader-following topology reduces recovery time by 32% compared to conventional topologies; (2) smaller communication ranges (4-6 vehicles) optimize stability in urban roads, while larger ranges (8-10 vehicles) suit highways; (3) delay time tolerance thresholds depend on platoon size and topology complexity. These findings provide actionable guidelines for designing robust CAV platoon systems under real-world communication constraints.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-08-18
DOI
https://doi.org/10.1371/journal.pone.0328555
Citations
1
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling and analyzing V2V communication limitations impacts on connected and automated vehicle platoons

Yulu Dai, Aiqing Yang, Yanbin Liu, Hu L
1 citations
PLoS ONE
Traffic control and management
1.21
article

Modeling and analyzing V2V communication limitations impacts on connected and automated vehicle platoons

Yulu Dai, Aiqing Yang, Yanbin Liu, Hu L
article en
1 citations

Abstract

Connected and automated vehicle (CAV) platooning, enabled by Vehicle-to-Vehicle (V2V) communication, promises significant improvements in traffic safety, throughput, and energy efficiency. However, communication constraints - such as range limitations and intermittent connectivity - disrupt information flow, destabilizing platoon dynamics. Existing models lack a unified framework to analyze how these constraints propagate through CAV interactions. To address this gap, the Platoon Intelligent Driver Model (PIDM) is proposed, a novel analytical framework that integrates dynamic communication topologies (predecessor-following, predecessor-leader-following, and 𝕜-predecessor-leader-following) with vehicle dynamics under V2V limitations. The PIDM enables systematic stability analysis and quantifies disturbance propagation mechanisms. Through numerical simulations, the study demonstrates that: (1) 𝕜-predecessor-leader-following topology reduces recovery time by 32% compared to conventional topologies; (2) smaller communication ranges (4-6 vehicles) optimize stability in urban roads, while larger ranges (8-10 vehicles) suit highways; (3) delay time tolerance thresholds depend on platoon size and topology complexity. These findings provide actionable guidelines for designing robust CAV platoon systems under real-world communication constraints.

PLoS ONEVol. 20(8)
Hohai University (CN), Hangzhou Vocational and Technical College (CN), Zhejiang University (CN), Tsinghua University (CN)
Laboratory of Nonferrous Metal Material and Processing Engineering of Anhui Province
Openalex Percentile: Top 18%
Traffic control and management
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.