Towards a Framework for Safety Assurance of Autonomous Systems

John McDermid, Yan Jia, Ibrahim Habli
2019 International Joint Conference on Artificial Intelligence  
Autonomous systems have the potential to provide great benefit to society. However, they also pose problems for safety assurance, whether fully autonomous or remotely operated (semi-autonomous). This paper discusses the challenges of safety assurance of autonomous systems and proposes a novel framework for safety assurance that, inter alia, uses machine learning to provide evidence for a system safety case and thus enables the safety case to be updated dynamically as system behaviour evolves.
dblp:conf/ijcai/McDermidJH19 fatcat:5zfjqsfiendqvbe3dcend7522m