Collision avoidance in 5G using MEC and NFV: The vulnerable road user safety use case

Automotive is considered one of the driving use cases for the 5th Generation (5G) systems, which currently formulates numerous scenarios and Key Performance Indicators (KPIs), via advanced Vehicle-to-everything (V2X) services and applications. Minimum end-to-end delay, as well as advanced contextual awareness requirements, pose novel architectural and functional challenges. This paper exploits two key enablers, namely Multiple Access/Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), and acts in a two-fold manner: Firstly, it proposes a hybrid architecture for 5G systems, which exploits the afore-mentioned technologies, and performs computing resources' selection among MEC and/or centralized, cloud-based resources (as VNFs), towards efficient service orchestration. The second contribution of this paper is a novel V2X service and algorithm, namely VRU-safe, that operates on top of the proposed architecture. VRU-Safe is an efficient, lightweight, low time complexity scheme, capable of identifying and predicting potential imminent road hazards between moving vehicles and Vulnerable Road Users (VRUs). The performance and viability of the proposed solutions are evaluated in a real-world 5G testbed in Europe.