Machine Learning for Vehicular Networks

TL;DR

This paper reviews recent advances in applying machine learning techniques to vehicular networks, highlighting how AI-driven data methods can enhance safety, efficiency, and autonomous driving in the context of 5G technology.

Contribution

It provides a comprehensive overview of machine learning applications in vehicular networks and discusses open challenges for future research.

Findings

Machine learning improves data analysis in vehicular networks.

AI techniques enhance safety and efficiency in vehicle communication.

Open issues include data privacy and real-time processing challenges.

Abstract

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, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area. After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks. We finally discuss and highlight several open issues that warrant further research.