Developing operator capacity estimates for supervisory control of autonomous vehicles.

Journal Article (Journal Article)


This study examined operators' capacity to successfully reallocate highly autonomous in-flight missiles to time-sensitive targets while performing secondary tasks of varying complexity.


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.


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.


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.


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.


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.

Full Text

Duke Authors

Cited Authors

  • Cummings, ML; Guerlain, S

Published Date

  • February 2007

Published In

Volume / Issue

  • 49 / 1

Start / End Page

  • 1 - 15

PubMed ID

  • 17315838

Electronic International Standard Serial Number (EISSN)

  • 1547-8181

International Standard Serial Number (ISSN)

  • 0018-7208

Digital Object Identifier (DOI)

  • 10.1518/001872007779598109


  • eng