Name
Title | ||
|---|---|---|
Comprehensive Survey on Machine Learning in Vehicular Network: Technology, Applications and Challenges |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/COMST.2021.3089688 | IEEE Communications Surveys & Tutorials |
Keywords | DocType | Volume |
V2X,vehicular network,machine learning,deep learning,V2V,Internet of Vehicles (IoV),resource allocation,routing,security,mobile cloud computing (MEC) | Journal | 23 |
Issue | Citations | PageRank |
3 | 14 | 0.54 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengxiao Tang | 1 | 253 | 11.24 |
| Bomin Mao | 2 | 265 | 13.95 |
| Nei Kato | 3 | 3982 | 263.66 |
| Guan Gui | 4 | 641 | 102.53 |