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Utility of Visual and Auditory Warning Alerting for Traffic Avoidance During UAS OperationsThis presentation covers the results of a human-in-the-loop simulation that examined the effect of caution versus warning-level alerting in the context of Unmanned Aircraft System (UAS) operations in civil airspace. Minimum Operational Performance Standards (MOPS) are currently being developed by RTCA Special Committee 228 for UAS Detect-and-Avoid (DAA) systems. Input from subject matter experts and multiple research studies have informed display requirements for DAA systems aimed at supporting timely and appropriate pilot responses to collision hazards. DAA alerting is designed to inform pilots of potential threats to DAA "well clear"; the two highest alert levels - caution and warning - indicate how soon pilot action is required and whether there is adequate time to coordinate with the air traffic controller (ATC). Additional empirical support is needed to clarify the extent to which warning-level alerting impacts DAA task performance. The present study explores the differential effects of the auditory and visual cues provided by the DAA Warning alert, and performance implications compared to caution-only alerting are discussed.
Document ID
20180007843
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Monk, Kevin J.
(NASA Ames Research Center Moffett Field, CA, United States)
Rorie, Robert C.
(NASA Ames Research Center Moffett Field, CA, United States)
Roberts, Zachary S.
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
November 27, 2018
Publication Date
October 1, 2018
Subject Category
Air Transportation And Safety
Report/Patent Number
ARC-E-DAA-TN61717
Meeting Information
Meeting: HFES 2018 International Annual Meeting
Location: Philadelphia, PA
Country: United States
Start Date: October 1, 2018
End Date: October 5, 2018
Sponsors: Human Factors and Ergonomics Society
Funding Number(s)
WBS: WBS 425425.04.02.01.01
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
detect and avoid
unmanned aircraft systems
human systems integration
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