Towards enhancement of unmanned aerial vehicle (UAV) operators' situation awareness: How often should their command and control map be updated

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Operators of military Unmanned Aerial Vehicles (UAVs) work in dynamic environments, where they must use shared command and control (C2) maps to orient, plan and perform their work. The map is overloaded with information that is irrelevant to their immediate operational mission. This clutter may harm their situation awareness (SA) and increase workload. An intelligent and dynamic filter algorithm has been developed to reduce the clutter by filtering information items on the map based on the environmental context. Implementing it raises questions regarding the update rate of the map filter. Two update rates were tested and their effect on UAV operators' workload and SA was examined empirically. Operators benefited from higher update rates in terms of SA and workload. This is an important step towards the development of the algorithm, which conceptualizes how intelligent algorithms can be used to improve human operators' interaction with autonomous systems.

Original languageEnglish
Title of host publicationHRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages538-540
Number of pages3
ISBN (Electronic)9781450370578
DOIs
StatePublished - 23 Mar 2020
Event15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 - Cambridge, United Kingdom
Duration: 23 Mar 202026 Mar 2020

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020
Country/TerritoryUnited Kingdom
CityCambridge
Period23/03/2026/03/20

Keywords

  • Command and control
  • Machine learning
  • Situation awareness
  • Unmanned aerial vehicle
  • Workload

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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