Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications.

Deep neural networks (DNNs) are widely considered as a key technology for perception in high and full driving automation. However, their safety assessment remains challenging, as they exhibit specific insufficiencies: black-box nature, simple performance issues, incorrect internal logic, and instability. These are not sufficiently considered in existing standards on safety argumentation. In this paper, we systematically establish and break down safety requirements to argue the sufficient absence of risk arising from such insufficiencies. We furthermore argue why diverse evidence is highly relevant for a safety argument involving DNNs, and classify available sources of evidence. Together, this yields a generic approach and template to thoroughly respect DNN specifics within a safety argumentation structure. Its applicability is shown by providing examples of methods and measures following an example use case based on pedestrian detection.