Assurance Case Patterns for Cyber-Physical Systems with Deep Neural Networks.

With the increasing use of deep neural networks (DNNs) in the safety-critical cyber-physical systems (CPS), such as autonomous vehicles, providing guarantees about the safety properties of these systems becomes ever more important. Tools for reasoning about the safety of DNN-based systems have started to emerge. In this paper, we show that assurance cases can be used to argue about the safety of CPS with DNNs by proposing assurance case patterns that are amenable to the existing evidence generation tools for these systems. We use case studies of two different autonomous driving scenarios to illustrate the use of the proposed patterns for the construction of these assurance cases.