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The black-box behavior of Convolutional Neural Networks is one of the biggest obstacles to the development of a standardized validation process. Methods for analyzing and validating neural networks currently rely on approaches and metrics provided by the scientific community without considering functional safety requirements. However, automotive norms, such as ISO26262 and ISO/PAS21448, do require a comprehensive knowledge of the system and of the working environment in which the network will be deployed. In order to gain such a knowledge and mitigate the natural uncertainty of probabilistic models, we focused on investigating the influence of filter weights on the classification confidence in Single Point Of Failure fashion. We laid the theoretical foundation of a method called the Neurons’ Criticality Analysis. This method, as described in this article, helps evaluate the criticality of the tested network and choose related plausibility mechanism. Copyright © 2021, for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). |
