A DL-based estimation probability approach for VRU collision avoidance

Assisted/autonomous driving is nowadays a vivid sector for both research and industry, thanks to the advances made using artificial intelligence and the pervasive and fast communication achieved by the fifth generation (5G) of cellular networks. A fully connected environment where traveling on public roads is done with limited (or without) human intervention may increase road safety. In this work, we present a system to detect possible collisions among vehicles and between pedestrians and vehicles with the final aim of reduce traffic accidents. Our proposal is based on a trajectory prediction algorithm plus a method to estimate the collision probability. Deep learning and Monte Carlo algorithms are used, respectively. The promising results open future research extensions.