Title
Classification of flight phases based on pilots' visual scanning strategies
Author
Peysakhovich, V.
Ledegang, W.
Houben, M.
Groen, E.
Contributor
Spencer, S.N. (editor)
Publication year
2022
Abstract
Eye movements analysis has great potential for understanding operator behaviour in many safety-critical domains, including aviation. In addition to traditional eye-tracking measures on pilots’ visual behavior, it seems promising to incorporate machine learning approaches to classify pilots’ visual scanning patterns. However, given the multitude of pattern measures, it is unclear which are better suited as predictors. In this study we analyzed the visual behaviour of eight pilots, flying different flight phases in a moving-base flight simulator. With this limited dataset we present a methodological approach to train linear Support Vector Machine models, using different combinations of the attention ratio and scanning pattern features. The results show that the overall accuracy to classify the pilots’ visual behaviour in different flight phases, improves from 51.6% up to 64.1% when combining the attention ratio and instrument scanning sequence in the classification model.
Subject
Aviation
Classification
Eye tracking
Flight simulators
Safety engineering
Scanning
Support vector machines
Eye movement analysis
Flight phase
Machine-learning
Phase based
Safety-critical domain
Scan patterns
Scanning strategies
Viewing behavior
Visual behavior
Visual scanning
Eye movements
To reference this document use:
http://resolver.tudelft.nl/uuid:3d2b62c1-bd1c-4450-a467-7d409713bc16
DOI
https://doi.org/10.1145/3517031.3529641
TNO identifier
980752
Publisher
Association for Computing Machinery
ISBN
9781450392525
Source
Eye Tracking Research and Applications Symposium (ETRA), 2022 ACM Symposium on Eye Tracking Research and Applications, ETRA 2022, 8 June 2022 through 11 June 2022
Document type
conference paper