Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload

Human operators supervising multiple uninhabited air and ground vehicles (UAVs and UGVs) under high task load must be supported appropriately in context by automation. Two experiments examined the efficacy of such adaptive automation in a simulated high workload reconnaissance mission involving four...

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Bibliographic Details
Main Authors: Parasuraman, Raja, Cosenzo, Keryl A, De Visser, Ewart
Format: Report
Language:English
Subjects:
ACCURACY
ADAPTIVE SYSTEMS
AUTOMATION
CHANGE DETECTION
DRONES
EFFICACY
GROUND VEHICLES
MISSIONS
Operations Research
OPERATORS(PERSONNEL)
PERFORMANCE(HUMAN)
Personnel Management and Labor Relations
Pilotless Aircraft
REAL TIME
RECONNAISSANCE
REPRINTS
SENSITIVITY
SITUATIONAL AWARENESS
SUBTASKS
TARGET CLASSIFICATION
TARGET RECOGNITION
UNMANNED
WORKLOAD
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Summary:Human operators supervising multiple uninhabited air and ground vehicles (UAVs and UGVs) under high task load must be supported appropriately in context by automation. Two experiments examined the efficacy of such adaptive automation in a simulated high workload reconnaissance mission involving four subtasks: (a) UAV target identification; (b) UGV route planning; (c) communications, with embedded verbal situation awareness probes; and (d) change detection. The results of the first baseline experiment established the sensitivity of a change detection procedure to transient and nontransient events in a complex, multi-window, dynamic display. Experiment 1 also set appropriate levels of low and high task load for use in Experiment 2, in which three automation conditions were compared: manual; static automation, in which an automated target recognition (ATR) system was provided for the UAV task; and adaptive automation, in which individual operator change detection performance was assessed in real time and used to invoke the ATR if and only if change detection accuracy was below a threshold. Change detection accuracy and situation awareness were higher and workload was lower for both automation conditions compared to manual performance. In addition, these beneficial effects on change detection and workload were significantly greater for adaptive compared to static automation. The results point to the efficacy of adaptive automation for supporting the human operator tasked with supervision of multiple uninhabited vehicles under high workload conditions. Published in the Journal of Military Psychology v21 n2 p270-297, 2009.