Architecting Dependable Learning-enabled Autonomous Systems: A Survey
It addresses the problem of ensuring reliability and safety in autonomous systems, particularly for automated driving, by reviewing existing methods without introducing new techniques, making it incremental.
This survey summarizes architectural approaches for building dependable learning-enabled autonomous systems, focusing on automated driving, and identifies three key technology pillars: diverse redundancy, information fusion, and runtime monitoring, along with promising research directions.
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.