Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
This addresses safety concerns in autonomous driving perception, but it appears incremental as it builds on existing safety processes without introducing a fundamentally new approach.
The paper tackles the challenge of ensuring safety in Deep Neural Network (DNN)-based perception for highly automated driving systems by proposing a framework to describe and mitigate DNN insufficiencies and derive safety mechanisms, with a categorization scheme for evaluation metrics to assess their effectiveness.
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.