Tracking the Unseen and Unaware: Deciphering Controllers' Detection Failures to Warnings Through Eye-Tracking Metrics

The integration of digital towers in air traffic control (ATC) intensifies visual complexity of controllers, increasing the risk of detection failure (DF) to warnings and compromising airspace safety. The inherent variability in human situational awareness and behaviors further complicates the diffe...

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Bibliographic Details
Published in:International journal of human-computer interaction Vol. 41; no. 19; pp. 11895 - 11914
Main Authors: Li, Zhimin, Li, Fan, Lyu, Mengtao
Format: Journal Article
Language:English
Published: Norwood Taylor & Francis 02.10.2025
Lawrence Erlbaum Associates, Inc
Subjects:
Acknowledgment
Air safety
Air traffic control
Aviation
Classification
Consciousness
detection failures recognition
Differentiation
Eye fixation
Eye movements
Eye tracking
Human technology relationship
Human-computer interaction
Induction
Operators
Perception
Perceptions
Real time
Recognition
Safety
Situational awareness
Towers
Tracking
Warning detection
warning frequency
Warnings
ISSN:1044-7318, 1532-7590, 1044-7318
Online Access:Get full text
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Summary:The integration of digital towers in air traffic control (ATC) intensifies visual complexity of controllers, increasing the risk of detection failure (DF) to warnings and compromising airspace safety. The inherent variability in human situational awareness and behaviors further complicates the differentiation and recognition of various DFs. This study deciphers DF by categorizing it into types based on Endsley's situation awareness theory, identifying specific causes and key indicators. A four-phase framework-DF classification, DF induction experiment, gaze dynamics analytics, and DF-type recognition-was applied to gaze data from 26 subjects. Results revealed distinct gaze patterns for non-perception, unaware perception, and aware perception of warnings, with continuous warnings weakening operators' awareness but enhancing foresight of warning implications. A random forest model achieved 80% precision in DF-type recognition, offering empirical support for real-time DF recognition and targeted interventions to improve visual warning detection and human-computer interaction in aviation safety.
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ISSN:1044-7318
1532-7590
1044-7318
DOI:10.1080/10447318.2024.2448877