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arXiv · 1712.07143

Machine Learning for Vehicular Networks

Hao Ye·Le Liang·Geoffrey Ye Li·JoonBeom Kim·Lu Lu·May Wu
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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.

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Hao Ye, Le Liang, Geoffrey Ye Li, JoonBeom Kim, Lu Lu, May Wu. 2017-12-19. Machine Learning for Vehicular Networks. https://arxiv.org/abs/1712.07143

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