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

• URL: http://arxiv.org/abs/2002.08935v1
• Date: Thu, 20 Feb 2020 18:32:53 GMT
• Title: Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
• Authors: Timo S\"amann, Peter Schlicht, Fabian H\"uger
• Abstract summary: Safety is one of the most important development goals for automated driving systems. For these, large parts of the traditional safety processes and requirements are not fully applicable or sufficient. This paper presents a framework for the description and mitigation of DNN insufficiencies and the derivation of relevant safety mechanisms.
• Score: 6.140206215951371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception function driven by Deep Neural Networks (DNNs). For these, large parts of the traditional safety processes and requirements are not fully applicable or sufficient. The aim of this paper is to present a framework for the description and mitigation of DNN insufficiencies and the derivation of relevant safety mechanisms to increase the safety of DNNs. To assess the effectiveness of these safety mechanisms, we present a categorization scheme for evaluation metrics.

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