  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.
