  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.
