Building the LeM2*R3 Model of Pilot Trust and Dynamic Workload Allocation. A Transition of Theory and Empirical Observations to Cockpit Demonstration

Abstract

For pilots to accept active decision aids during complex flight scenarios, it is essential that the automation work is in synergy with aircrew. To accomplish this, the automation must go well beyond menu and macro selections, where the pilot must explicitly tell the automation what to do and when to do it. It must also transcend "mother may I" approaches, where the automation asks for permission to proceed. To these traditional barriers, the automation needs a sense of how the pilot will react in a given situation and, based on that reaction, how much of the workload could be allocated to the automation at any given time. For this purpose, the authors reviewed the literature on human factors and dynamic function allocation. This literature provided a wealth of information on this topic. Based on the current state of the art in this topic area, the authors developed and tested a dynamic model of pilot trust and workload allocation. This "full degrees of freedom" model transitions human factors theory, as it exists today, into an engineering application. The resulting model can be combined with other information obtained from static and continuous processes to divide the workload and minimize cognitive overload.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1998
Accession Number
ADA350481

Entities

People

  • John M. Reising
  • Peter G. Raeth

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Artificial Intelligence
  • Automation
  • Computer Programming
  • Computers
  • Control Systems
  • Engineering
  • Human Behavior
  • Human Factors Engineering
  • Human-Machine Systems
  • Psychology
  • Reliability
  • Simulations
  • Social Psychology
  • Workload

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Aviation Science / Aeronautics.
  • Software Engineering.