title: Developing operator capacity estimates for supervisory control of autonomous vehicles.
abstract: OBJECTIVE
  This study examined operators' capacity to successfully reallocate highly autonomous in-flight missiles to time-sensitive targets while performing secondary tasks of varying complexity.
  
  
  BACKGROUND
  Regardless of the level of autonomy for unmanned systems, humans will be necessarily involved in the mission planning, higher level operation, and contingency interventions, otherwise known as human supervisory control. As a result, more research is needed that addresses the impact of dynamic decision support systems that support rapid planning and replanning in time-pressured scenarios, particularly on operator workload.
  
  
  METHODS
  A dual screen simulation that allows a single operator the ability to monitor and control 8, 12, or 16 missiles through high level replanning was tested on 42 U.S. Navy personnel.
  
  
  RESULTS
  The most significant finding was that when attempting to control 16 missiles, participants' performance on three separate objective performance metrics and their situation awareness were significantly degraded.
  
  
  CONCLUSIONS
  These results mirror studies of air traffic control that demonstrate a similar decline in performance for controllers managing 17 aircraft as compared with those managing only 10 to 11 aircraft. Moreover, the results suggest that a 70% utilization (percentage busy time) score is a valid threshold for predicting significant performance decay and could be a generalizable metric that can aid in manning predictions.
  
  
  CONCLUSIONS
  This research is relevant to human supervisory control of networked military and commercial unmanned vehicles in the air, on the ground, and on and under the water.
authors.affiliation: Humans and Automation Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. missyc@mit.edu
authors.forename: M L
authors.initials: ML
authors.lastname: Cummings
authors.forename: Stephanie
authors.initials: S
authors.lastname: Guerlain
mesh_terms: D000328:Adult; D000367:Age Factors; D000401:Aircraft; D001331:Automation; D001359:Aviation; D006801:Humans; D008875:Middle Aged; D008887:Military Medicine; D009934:Organization and Administration; D013647:Task Performance and Analysis; D014481:United States; D014584:User-Computer Interface; D016526:Workload