Identifying Factors That Impact Levels of Automation in Autonomous Systems

Abstract

The need to support complex human and machine collaboration has increased because of recent advances in the use of software and artificial intelligence approaches across various application domains. Building applications with more autonomy has grown dramatically as modern system development capability has significantly improved. However, understanding how to assign duties between humans and machines still needs improvement, and there is a need for better approaches to apportion these tasks. Current methods do not make adaptive automation easy, as task assignments during system operation need to take knowledge about the optimal level of automation (LOA) into account during the collaboration. There is currently a lack of explicit knowledge regarding the factors that influence the variability of human-system interaction and the correct LOA. Additionally, models have not been provided to represent the adaptive LOA variation based on these parameters and their interactions and interdependencies. The study, presented in this paper, based on an extensive literature review, identifies and classifies the factors that affect the degree of automation in autonomous systems. It also proposes a model based on feature diagrams representing the factors and their relationships with LOAs. With the support of two illustrative examples, we demonstrate how to apply these factors and how they relate to one another. This work advances research in the design of autonomous systems by offering an adaptive automation approach that can suggest levels of automation to facilitate human-computer interactions.