$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios
This addresses the need for reliable network slices in V2X scenarios to support autonomous driving and reduce road casualties, though it is an incremental improvement focused on specific emergency situations.
The paper tackles the problem of guaranteeing service delivery for mission-critical V2X services during exceptional events like road accidents by proposing π-ROAD, a deep learning framework that detects and classifies non-recurring road events, enabling proactive instantiation of emergency network slices and reducing their impact on existing services by up to 30%.
Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated \emph{network slices} are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose $π$-ROAD, a \emph{deep learning} framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. $π$-ROAD enables operators to \emph{proactively} instantiate dedicated \emph{Emergency Network Slices (ENS)} as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within $400~km$ of a highway in Europe and augmented with publicly available information on related road events. Our results show that $π$-ROAD successfully detects and classifies non-recurring road events and reduces up to $30\%$ the impact of ENS on already running services.