  Benefiting from the widely deployed LTE infrastructures, the fifth generation
(5G) wireless networks have been becoming a critical enabler for the emerging
vehicle-to-everything (V2X) communications. However, existing LTE networks
cannot efficiently support stringent but dynamic requirements of V2X services.
One effective solution to overcome this challenge is network slicing, whereby
different services could be supported by logically separated networks. To
mitigate the increasing complexity of network slicing in 5G, we propose to
leverage the recent advancement of Machine Learning (ML) technologies for
automated network operation. Specifically, we propose intelligent network
slicing architecture for V2X services, where network functions and
multi-dimensional network resources are virtualized and assigned to different
network slices. In achieving optimized slicing intelligently, several critical
techniques, including mobile data collection and ML algorithm design, are
discussed to tackle the related challenges. Then, we develop a simulation
platform to illustrate the effectiveness of our proposed intelligent network
slicing. With integration of 5G network slicing and ML-enabled technologies,
the QoS of V2X services is expected to be dramatically enhanced.
