Adaptable and Adaptive Automation for Supervisory Control of Multiple Autonomous Vehicles

Abstract

Supervisory control of multiple autonomous vehicles raises many issues concerning the balance of system autonomy with human interaction for optimal operator situation awareness and system performance. An unmanned vehicle simulation designed to manipulate the application of automation was used to evaluate participants' performance on image analysis tasks under two automation control schemes: adaptable (level of automation directly manipulated by participant throughout trials) and adaptive (level of automation adapted as a function of participants? performance on four types of tasks). The results showed that while adaptable automation increased workload, it also improved change detection, as well as operator confidence in task-related decision-making.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2012
Accession Number
ADA569836

Entities

People

  • Brian Kidwell
  • Gloria L. Calhoun
  • Heath A. Ruff
  • Raja Parasuraman

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Automation
  • Autonomous Vehicles
  • Change Detection
  • Cognitive Workload
  • Control Systems
  • Military Research
  • Operating Systems
  • Psychology
  • Situational Awareness
  • Supervisory Control
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles
  • Vehicles
  • Workload

Readers

  • Artificial Intelligence
  • Distributed Systems and Data Platform Development

Technology Areas

  • Autonomy
  • Autonomy - Human-Robot Interaction