  Can engineering neural networks be approached in a disciplined way similar to
how engineers build software for civil aircraft? We present
nn-dependability-kit, an open-source toolbox to support safety engineering of
neural networks for autonomous driving systems. The rationale behind
nn-dependability-kit is to consider a structured approach (via Goal Structuring
Notation) to argue the quality of neural networks. In particular, the tool
realizes recent scientific results including (a) novel dependability metrics
for indicating sufficient elimination of uncertainties in the product life
cycle, (b) formal reasoning engine for ensuring that the generalization does
not lead to undesired behaviors, and (c) runtime monitoring for reasoning
whether a decision of a neural network in operation is supported by prior
similarities in the training data. A proprietary version of
nn-dependability-kit has been used to improve the quality of a level-3
autonomous driving component developed by Audi for highway maneuvers.
