Design Considerations and Research Needs for Expanding the Current Perceptual Model of Spatial Orientation into an In-Cockpit Spatial Disorientation Warning System

Abstract

Spatial disorientation (SD) in flight occurs when a pilot incorrectly perceives the orientation or motion of the aircraft, due to vestibular, somatosensory, or visual illusions. Mathematical models of SD predict a pilots perceived orientation, based on quantitative analysis of external and internal factors, e.g., a resultant gravitoinertial force exerted on a pilots body versus the vestibular response to the force. The current application of mathematical modeling involves analyzing flight mishaps post-hoc to determine whether the pilot likely experienced SD in the moments prior to the mishap. Lawson, McGrath, Newman, and Rupert (2015) propose applying current modeling principles to the creation of an in-cockpit warning system to allow for proactive prediction and pilot warning of imminent SD and prevention of SD-related mishaps. The present report discusses the feasibility, desirability, and design considerations of the proposed expansion of the current model into an in-cockpit SD warning system.

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 2016
Accession Number
AD1031427

Entities

People

  • Angus H. Rupert
  • Ben D. Lawson
  • Braden J. Mcgrath
  • John C Brill
  • Linda-brooke I Thompson
  • Michael C. Newman

Organizations

  • United States Army Aeromedical Research Lab

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Accidents
  • Aircrafts
  • Cognition
  • Collision Avoidance Systems
  • Control Systems
  • Employment
  • Engineering
  • False Alarms
  • Governments
  • Human-Machine Interaction
  • Instructions
  • Military Aircraft
  • Neurobehavioral Manifestations
  • Personnel Management
  • Safety
  • Warning Systems
  • Workload

Fields of Study

  • Psychology

Readers

  • Aviation Science / Aeronautics.
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computational Modeling and Simulation