Eye-Tracking Metrics for Task-Based Supervisory Control

TL;DR

This paper investigates the use of eye-tracking metrics to objectively evaluate and improve a task-based supervisory control interface for heterogeneous unmanned vehicles, aiming to enhance usability assessment methods.

Contribution

It introduces a pilot study applying eye-tracking to a supervisory control interface, exploring its potential to augment standard usability metrics and model operator behavior.

Findings

Eye-tracking provides valuable insights beyond traditional metrics.

Initial models of operator behavior were formulated.

The study highlights promising directions for future research.

Abstract

Task-based, rather than vehicle-based, control architectures have been shown to provide superior performance in certain human supervisory control missions. These results motivate the need for the development of robust, reliable usability metrics to aid in creating interfaces for use in this domain. To this end, we conduct a pilot usability study of a particular task-based supervisory control interface called the Research Environment for Supervisory Control of Heterogenous Unmanned Vehicles (RESCHU). In particular, we explore the use of eye-tracking metrics as an objective means of evaluating the RESCHU interface and providing guidance in improving usability. Our main goals for this study are to 1) better understand how eye-tracking can augment standard usability metrics, 2) formulate initial models of operator behavior, and 3) identify interesting areas of future research.