  We provide a summary over architectural approaches that can be used to
construct dependable learning-enabled autonomous systems, with a focus on
automated driving. We consider three technology pillars for architecting
dependable autonomy, namely diverse redundancy, information fusion, and runtime
monitoring. For learning-enabled components, we additionally summarize recent
architectural approaches to increase the dependability beyond standard
convolutional neural networks. We conclude the study with a list of promising
research directions addressing the challenges of existing approaches.
