Exploiting augmented intelligence in the modeling of safety-critical autonomous systems

Abstract

Machine Learning (ML) is used increasingly in safety-critical systems to provide more complex autonomy to make the system to do decisions by itself in uncertain environments. Using ML to learn system features is fundamentally different from manually implementing them in conventional components written in source code. In this paper, we make a first step towards exploring the architecture modeling of safety-critical autonomous systems which are composed of conventional components and ML components, based on natural language requirements. Firstly, Augmented Intelligence (AuI) for restricted natural language requirement modeling is proposed. In that, several AI technologies such as natural language processing and clustering are used to recommend candidate terms to the glossary, as well as machine learning is used to predict the category of requirements. The glossary including data dictionary and domain glossary and the category of requirements will be used in the restricted natural language requirement specification method RNLReq, which is equipped with a set of restriction rules and templates to structure and restrict the way how users document requirements. Secondly, automatic generation of SysML architecture models from the RNLReq requirement specifications is presented. Thirdly, the prototype tool is implemented based on Papyrus. Finally, it presents the evaluation of the proposed approach using an industrial Autonomous Guidance, Navigation and Control (AGNC) case study.

Artificial Intelligence and Software Modeling Approaches in Autonomous Vehicles for Safety Management: A Systematic Review

“…They encompass requirement gathering, risk assessing to ensure safety, and the extracting of pivotal and generic attributes to prevent accidents. For instance, in [30], the authors delved into the domain of safety management across various hierarchical levels. Their methodology involved leveraging augmented intelligence to construct safety models rooted in data characteristics.…”