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A DL-based estimation probability approach for VRU collision avoidance

Authors: Parada, R; Corvillo, R; Dini, P; IEEE;

A DL-based estimation probability approach for VRU collision avoidance

Abstract

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.

Keywords

Trajectory prediction, trajectory prediction, Collision avoidance, deep learning, Deep learning, 5G

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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