Vehicle-to-vehicle communication: design, performance, and disruption mitigation in real-world environment

Wang, Xinao (2024) Vehicle-to-vehicle communication: design, performance, and disruption mitigation in real-world environment. PhD thesis, University of Nottingham.

[img]
Preview
PDF (Thesis correction) (Thesis - as examined) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (4MB) | Preview

Abstract

This thesis investigates the performance of 802.11p-based V2V communication in real-life scenarios, and explores potential practical applications such as GNSS correction data broadcasting to improve the positioning accuracy of nearby vehicles, and enhancing communication robustness by preemptively predicting potential disruptions with the assistance of Machine Learning (ML) models. A custom V2V On-board Unit (OBU) hardware platform was developed, and real- world multi-vehicle outdoor experiments were planned and carried out. The collected data was examined and used to train a number of ML models, and their performance was compared.

The experiments revealed that the custom OBU was fully functional, and signal quality and communication range were observed to be affected by real-world imperfections. The GNSS correction data broadcasting was shown to notably increase the positioning accuracy of nearby vehicles, and the ML models trained from Key Performance Indicators (KPIs) demonstrated excellent prediction accuracy, allowing pre-emptive actions to be taken to reduce the downtime from communication disruption.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Qu, Rong
Meng, Xiaolin
Keywords: Vehicle-to-Vehicle Communication, V2X Communication, Autonomous Vehicles, Machine Learning, Preemptive Disruption Prediction
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculties/Schools: UK Campuses > Faculty of Engineering > Department of Civil Engineering
Item ID: 77198
Depositing User: Wang, Xinao
Date Deposited: 23 Apr 2024 09:58
Last Modified: 23 Apr 2024 09:58
URI: https://eprints.nottingham.ac.uk/id/eprint/77198

Actions (Archive Staff Only)

Edit View Edit View