# nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems

arXiv: https://arxiv.org/abs/1811.06746 · HTML: https://mlsanity.com/paper/1811.06746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components](https://mlsanity.com/paper/2202.05313/) (2022)

## Abstract (arXiv)

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.