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System-Wide Error Attribution in Multi-Vehicle Operations: Theoretical Explanation, Implications, and ApplicationsIn multi-vehicle aerial operation contexts, a single or several human operators (m) are responsible for managing multiple uncrewed vehicles (N). This is referred to as the m:N operational paradigm.

When automation error occurs during m:N operations, especially if multiple vehicles exhibit automation error, a question is raised: What level of system globality will the error be attributed to? Globality refers to the levels within the conceptual hierarchy
of a perceptual object.

Significant costs could be incurred if the error is falsely attributed to a higher globality level than it should be (e.g., multiple groups of vehicles as opposed to a single vehicle), and those vehicles are subsequently subject to grounding and evaluation/maintenance. Likewise, it could be problematic if error is falsely attributed to a component (i.e., a lower globality level), resulting in ongoing, unsafe operation of vehicles that should be grounded or evaluated.

Thus, to meet required safety standards and facilitate commercial viability, multi-vehicle operators must be capable of appropriately attributing the level of globality of automation error within the system they are responsible for.
Document ID
20240010968
Acquisition Source
Langley Research Center
Document Type
Poster
Authors
Elliot L Biltekoff
(Langley Research Center Hampton, United States)
Eric T Chancey
(Langley Research Center Hampton, United States)
Mike S Politowicz
(Langley Research Center Hampton, United States)
Date Acquired
August 23, 2024
Subject Category
Behavioral Sciences
Meeting Information
Meeting: HFES International Annual Meeting with the mission to Advance Systems and Practices through Innovation, Research and Education (ASPIRE)
Location: Phoenix, AZ
Country: US
Start Date: September 9, 2024
End Date: September 13, 2024
Sponsors: Human Factors and Ergonomics Society
Funding Number(s)
WBS: 109492.02.07.07.07
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Technical Review
Single Expert
Keywords
Error
Automation
AAM
UAM
Perception
m:N
Gestalt
Expertise
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