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Recent advances in the field of deep learning and impressive performance ofdeep neural networks (DNNs) for perception have resulted in an increased demandfor their use in automated driving (AD) systems. The safety of such systems isof utmost importance and thus requires to consider the unique properties ofDNNs.  In order to achieve safety of AD systems with DNN-based perception componentsin a systematic and comprehensive approach, so-called safety concerns have beenintroduced as a suitable structuring element. On the one hand, the concept ofsafety concerns is -- by design -- well aligned to existing standards relevantfor safety of AD systems such as ISO 21448 (SOTIF). On the other hand, it hasalready inspired several academic publications and upcoming standards on AIsafety such as ISO PAS 8800.  While the concept of safety concerns has been previously introduced, thispaper extends and refines it, leveraging feedback from various domain andsafety experts in the field. In particular, this paper introduces an additionalcategorization for a better understanding as well as enabling cross-functionalteams to jointly address the concerns.

Deep Learning Safety Concerns in Automated Driving Perception