Pilot Dependence on Imperfect Diagnostic Automation in Simulated UAV Flights: An Attentional Visual Scanning Analysis

Abstract

An unmanned air vehicle (UAV) simulation was designed to reveal the effects of imperfectly reliable diagnostic automation a monitor of system health parameters on pilot attention, as the latter was assessed via visual scanning. Four groups of participants flew a series of legs under different automation conditions: a baseline (no automation) control, and automation which was either 100% reliable, 60% reliable with a low-threshold bias to produce false alerts, and 60% reliable with a high threshold to produce misses. A high workload mission completion task and ground surveillance task were simultaneously imposed. Consistent with the reliance-compliance model of imperfect automation developed by Meyer (2001), miss-prone automation removed visual attention from the surveillance task, while FA-prone automation delayed the alert-driven attention shift to the system monitoring task.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA446167

Entities

People

  • Ben Hammer
  • Christopher D Wickens
  • Juliana Goh
  • Stephen Dixon

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Aircrafts
  • Automation
  • Control Systems
  • Detection
  • False Alarms
  • Flight Crews
  • Human Factors Engineering
  • Pilots
  • Psychology
  • Remotely Piloted Vehicles
  • Scanning
  • Task Performance And Analysis
  • Unmanned Aerial Vehicles
  • Vehicles
  • Warning Systems
  • Workload

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Sensor Fusion and Tracking Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • Autonomy
  • Autonomy - UAVs