Machine Learning for Vehicular Networks: Recent Advances and Application Examples

The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient and pave the path to autonomous driving in the advent of the fifth-generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, the authors review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this upcoming area.

• Record URL:
  https://doi.org/10.1109/MVT.2018.2811185
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15566072
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Ye, Hao
  • Liang, Le
  • Li, Geoffrey Y
  • Kim, JoonBeom
  • Lu, Lu
  • Wu, May
• Publication Date: 2018-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 94-101
• Serial:
  • IEEE Vehicular Technology Magazine
  • Volume: 13
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1556-6072
  • Serial URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=34840

Subject/Index Terms

• TRT Terms: Algorithms; Intelligent vehicles; Machine learning; Vehicular ad hoc networks; Wireless communication systems
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01671701
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 4 2018 4:53PM