  As wireless networks evolve towards high mobility and providing better
support for connected vehicles, a number of new challenges arise due to the
resulting high dynamics in vehicular environments and thus motive rethinking of
traditional wireless design methodologies. Future intelligent vehicles, which
are at the heart of high mobility networks, are increasingly equipped with
multiple advanced onboard sensors and keep generating large volumes of data.
Machine learning, as an effective approach to artificial intelligence, can
provide a rich set of tools to exploit such data for the benefit of the
networks. In this article, we first identify the distinctive characteristics of
high mobility vehicular networks and motivate the use of machine learning to
address the resulting challenges. After a brief introduction of the major
concepts of machine learning, we discuss its applications to learn the dynamics
of vehicular networks and make informed decisions to optimize network
performance. In particular, we discuss in greater detail the application of
reinforcement learning in managing network resources as an alternative to the
prevalent optimization approach. Finally, some open issues worth further
investigation are highlighted.
