nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
This addresses the need for disciplined safety engineering in neural networks for autonomous driving, though it appears incremental by applying existing engineering methods to this domain.
The authors tackled the problem of engineering neural networks for safety-critical autonomous driving systems by introducing nn-dependability-kit, an open-source toolbox that applies structured approaches like Goal Structuring Notation to argue neural network quality, resulting in its use to improve a level-3 autonomous driving component by Audi.
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.