Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.
@article{arxiv.1806.07635,
title = {Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning},
author = {Patrik Feth and Mohammed Naveed Akram and René Schuster and Oliver Wasenmüller},
journal= {arXiv preprint arXiv:1806.07635},
year = {2018}
}