Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning

Patrik Feth; Mohammed Naveed Akram; René Schuster; Oliver Wasenmüller

In: Proceedings of the 1st International Workshop on Artificial Intelligence Safety Engineering 2018 |. International Workshop on Artificial Intelligence Safety Engineering (WAISE-2018), located at SAFECOMP 2018, September 18, Västerås, Sweden, Springer, 2018.


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.

feth2018risk.pdf (pdf, 2 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence