Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges

Fengxiao Tang, Bomin Mao, Nei Kato, Guan Gui

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Towards future intelligent vehicular network, the machine learning as the promising artificial intelligence tool is widely researched to intelligentize communication and networking functions. In this paper, we provide a comprehensive survey on various machine learning techniques applied to both communication and network parts in vehicular network. To benefit reading, we first give a preliminary on communication technologies and machine learning technologies in vehicular network. Then, we detailedly describe the challenges of conventional techniques in vehicular network and corresponding machine learning based solutions. Finally, we present several open issues and emphasize potential directions that are worthy of research for the future intelligent vehicular network.

Original languageEnglish
Article number9463461
Pages (from-to)2027-2057
Number of pages31
JournalIEEE Communications Surveys and Tutorials
Volume23
Issue number3
DOIs
Publication statusPublished - 2021 Jul 1

Keywords

  • Deep learning
  • Internet of Vehicles (IoV)
  • Machine learning
  • Mobile cloud computing (MEC)
  • Resource allocation
  • Routing security
  • V2V
  • V2X
  • Vehicular network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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