Machine Learning for Vehicular Networks

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.

Accepted by IEEE Vehicular Technology Magazine

References in corpus (3)

• Deep Learning in Neural Networks: An Overview
• Reinforcement Learning for Resource Provisioning in Vehicular Cloud
• Vehicular Communications: A Network Layer Perspective

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• TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction
• Deep Learning-aided Application Scheduler for Vehicular Safety Communication
• Evolution of Vehicle Network on a Highway