$\pi$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

TL;DR

The paper introduces $$-ROAD, a deep learning framework that detects non-recurring road events in V2X scenarios, enabling proactive emergency network slicing to improve service reliability during critical incidents.

Contribution

It presents a novel deep learning-based approach for automatic detection and classification of road events, facilitating dynamic emergency network slice management in V2X communications.

Findings

Successfully detects and classifies non-recurring road events

Reduces impact of emergency slices on existing services by up to 30%

Validated with real mobile network traces and road event data

Abstract

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 $\pi$-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. $\pi$-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 $\pi$-ROAD successfully detects and classifies non-recurring road events and reduces up to $30\%$ the impact of ENS on already running services.