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

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


Gallina, B.; Skavhaug, A.; Schoitsch, E.; Bitsch, F.:
Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE : Västerås, Sweden, September 18, 2018. Proceedings
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11094)
ISBN: 978-3-319-99229-7
ISBN: 978-3-319-99228-0
ISBN: 978-3-319-99230-3
International Conference on Computer Safety, Reliability, and Security (SAFECOMP) <37, 2018, Västerås>
Fraunhofer IESE ()
Automation; Deep neural networks; Optical radar; Safety engineering; Stereo image processing; Vehicles; Automation levels; Driving situations; Dynamic risk assessments; Heterogeneous channels; Monocular image; Situation awareness; Stereo cameras; Traffic scene; Risk assessment

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.