title: Auditory decision aiding in supervisory control of multiple unmanned aerial vehicles.
abstract: OBJECTIVE
  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.
  
  
  BACKGROUND
  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).
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  This research has implications for supervisory control display design.
authors.affiliation: Massachusetts Institute of Technology, Humans and Automation Laboratory, 77 Massachusetts Ave., Room 33-407, Cambridge, MA 02139, USA. bdonmez@mit.edu
authors.forename: Birsen
authors.initials: B
authors.lastname: Donmez
authors.forename: M L
authors.initials: ML
authors.lastname: Cummings
authors.forename: Hudson D
authors.initials: HD
authors.lastname: Graham
mesh_terms: D000328:Adult; D001359:Aviation; D003661:Decision Support Techniques; D005260:Female; D006309:Hearing; D006801:Humans; D008297:Male; D008328:Man-Machine Systems; D008889:Military Personnel; D012449:Safety; D013647:Task Performance and Analysis; D055815:Young Adult