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Vehicle-to-Everything (V2X) requirements from cooperative autonomous driving can be characterized as ultra-reliable, low latency, high traffic, and high mobility. These requirements introduce great challenges in the air interface and protocol stack design, resource allocation, network deployment, and all the way up to mobile (or multi-access) edge computing (MEC), cloud and application layer. In this paper, we present a cooperative autonomous driving oriented MEC-aided 5G-V2X prototype system design and the rationale behind the design choices. The prototype system is developed based on a next-generation radio access network (NG-RAN) experimental platform, a cooperative driving vehicle platoon, and an MEC server providing high definition (HD) 3D dynamic map service. Field tests are conducted and the results demonstrate that the combination of 5G-V2X, MEC and cooperative autonomous driving can be pretty powerful. Considering the remaining challenges in the commercial deployment of 5G-V2X networks and future researches, we propose two artificial intelligence (AI) based optimization tools. The first is a deep-learning-based tool called deep spatio-temporal residual networks with a permutation operator (PST-ResNet). By providing city-wide user and network traffic prediction, PST-ResNet can help to reduce the capital expense (CAPEX) and operating expense (OPEX) costs of commercial 5G-V2X networks. The second is a swarm intelligence based optimization tool called subpopulation collaboration based dynamic self-adaption cuckoo Search (SC-DSCS), which can be widely used to solve complex optimization problems in future researches. The effectiveness of proposed optimization tools is verified by real-world data and benchmark functions.

Cooperative Autonomous Driving Oriented MEC-Aided 5G-V2X: Prototype System Design, Field Tests and AI-Based Optimization Tools