Auditory decision aiding in supervisory control of multiple unmanned aerial vehicles.

Published

Journal Article

This article is an investigation of the effectiveness of sonifications, which are continuous auditory alerts mapped to the state of a monitored task, in supporting unmanned aerial vehicle (UAV) supervisory control.UAV supervisory control requires monitoring a UAV across multiple tasks (e.g., course maintenance) via a predominantly visual display, which currently is supported with discrete auditory alerts. Sonification has been shown to enhance monitoring performance in domains such as anesthesiology by allowing an operator to immediately determine an entity's (e.g., patient) current and projected states, and is a promising alternative to discrete alerts in UAV control. However, minimal research compares sonification to discrete alerts, and no research assesses the effectiveness of sonification for monitoring multiple entities (e.g., multiple UAVs).The authors conducted an experiment with 39 military personnel, using a simulated setup. Participants controlled single and multiple UAVs and received sonifications or discrete alerts based on UAV course deviations and late target arrivals.Regardless of the number of UAVs supervised, the course deviation sonification resulted in reactions to course deviations that were 1.9 s faster, a 19% enhancement, compared with discrete alerts. However, course deviation sonifications interfered with the effectiveness of discrete late arrival alerts in general and with operator responses to late arrivals when supervising multiple vehicles.Sonifications can outperform discrete alerts when designed to aid operators to predict future states of monitored tasks. However, sonifications may mask other auditory alerts and interfere with other monitoring tasks that require divided attention.This research has implications for supervisory control display design.

Full Text

Duke Authors

Cited Authors

  • Donmez, B; Cummings, ML; Graham, HD

Published Date

  • October 2009

Published In

Volume / Issue

  • 51 / 5

Start / End Page

  • 718 - 729

PubMed ID

  • 20196296

Pubmed Central ID

  • 20196296

Electronic International Standard Serial Number (EISSN)

  • 1547-8181

International Standard Serial Number (ISSN)

  • 0018-7208

Digital Object Identifier (DOI)

  • 10.1177/0018720809347106

Language

  • eng