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

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Abstract

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.

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... We have completed and adapted this approach to integrate the specific properties (safe behaviour reference given by the LUT) and the safety net. [26] proposed a template to structure the safety argumentation part specific to DNNs. Their work is illustrated with an example use case based on pedestrian detection. ...
Chapter
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