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Vehicle-to-everything (V2X) is expected to become one of the main drivers of5G business in the near future. Dedicated \emph{network slices} are envisionedto satisfy the stringent requirements of advanced V2X services, such asautonomous driving, aimed at drastically reducing road casualties. However, asV2X services become more mission-critical, new solutions need to be devised toguarantee 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 trafficpatterns along roads, detect non-recurring events and classify them by severitylevel. $\pi$-ROAD enables operators to \emph{proactively} instantiate dedicated\emph{Emergency Network Slices (ENS)} as needed while re-dimensioning theexisting slices according to their service criticality level. Our framework isvalidated by means of real mobile network traces collected within $400~km$ of ahighway in Europe and augmented with publicly available information on relatedroad events. Our results show that $\pi$-ROAD successfully detects andclassifies non-recurring road events and reduces up to $30\%$ the impact of ENSon already running services.

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