Data-Driven Modeling and Analysis Framework for Cockpit Human–Machine Interaction Issues

Abstract

As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human–machine interactions have been cited as one of the main causes behind many aviation accidents. The complex nature of a pilot’s interactions with an autopilot system makes it challenging to identify all possible actions that may lead to an human–machine interaction-related incident. In this paper, a data-driven analysis tool is proposed to detect and categorize potential human–machine interaction issues in large-scale flight operational quality assurance datasets. The proposed tool is developed using a multilevel clustering framework, where a set of basic clustering techniques are combined with a consensus-based approach to group human–machine interaction events and create a data-driven model from the flight operational quality assurance data. The proposed framework is able to effectively compress a large dataset into a small set of representative clusters within a data-driven model, enabling subject matter experts to effectively investigate potential human–machine interaction issues.