Deep Learning Safety Concerns in Automated Driving Perception
Stephanie Abrecht Alexander Hirsch Shervin Raafatnia Matthias Woehrle
Recent advances in the field of deep learning and impressive performance of
deep neural networks (DNNs) for perception have resulted in an increased demand
for their use in automated driving (AD) systems. The safety of such systems is
of utmost importance and thus requires to consider the unique properties of
DNNs.
  In order to achieve safety of AD systems with DNN-based perception components
in a systematic and comprehensive approach, so-called safety concerns have been
introduced as a suitable structuring element. On the one hand, the concept of
safety concerns is -- by design -- well aligned to existing standards relevant
for safety of AD systems such as ISO 21448 (SOTIF). On the other hand, it has
already inspired several academic publications and upcoming standards on AI
safety such as ISO PAS 8800.
  While the concept of safety concerns has been previously introduced, this
paper extends and refines it, leveraging feedback from various domain and
safety experts in the field. In particular, this paper introduces an additional
categorization for a better understanding as well as enabling cross-functional
teams to jointly address the concerns.