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Safety is one of the most important development goals for highly automateddriving (HAD) systems. This applies in particular to the perception functiondriven by Deep Neural Networks (DNNs). For these, large parts of thetraditional safety processes and requirements are not fully applicable orsufficient. The aim of this paper is to present a framework for the descriptionand mitigation of DNN insufficiencies and the derivation of relevant safetymechanisms to increase the safety of DNNs. To assess the effectiveness of thesesafety mechanisms, we present a categorization scheme for evaluation metrics.

Strategy to Increase the Safety of a DNN-based Perception for HAD Systems