  Intersection crossing represents one of the most dangerous sections of the
road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary
solution to the problem. In this work, we present a novel framework that
detects preemptively collisions at urban crossroads, exploiting the
Multi-access Edge Computing (MEC) platform of 5G networks. At the MEC, an
Intersection Manager (IM) collects information from both vehicles and the road
infrastructure to create a holistic view of the area of interest. Based on the
historical data collected, the IM leverages the capabilities of an
encoder-decoder recurrent neural network to predict, with high accuracy, the
future vehicles' trajectories. As, however, accuracy is not a sufficient
measure of how much we can trust a model, trajectory predictions are
additionally associated with a measure of uncertainty towards confident
collision forecasting and avoidance. Hence, contrary to any other approach in
the state of the art, an uncertainty-aware collision prediction framework is
developed that is shown to detect well in advance (and with high reliability)
if two vehicles are on a collision course. Subsequently, collision detection
triggers a number of alarms that signal the colliding vehicles to brake. Under
real-world settings, thanks to the preemptive capabilities of the proposed
approach, all the simulated imminent dangers are averted.
