This master thesis deals with the issue of pilot inattention and proposes a design of a system for detecting inattention of general aviation pilots. Inattention belongs to one of the human-caused errors that currently contribute to the most common causes of aviation accidents. The theoretical part deals with the definition of inattention, compares different aviation categories based on flight rules, and contains a search of detection methods. The practical part of the work deals with the selection of suitable sensors, data collection, and implementation of detection algorithms. In this thesis, two different approaches were chosen. The first implementing machine learning using the RUSBoost classifier, which detects states of attention and distraction. The second approach represents the design of a system for detecting pilot inattention based on a set of rules specified in the CLIPS expert system.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:446772 |
| Date | January 2021 |
| Creators | Novotný, Josef |
| Contributors | Mekyska, Jiří, Smékal, Zdeněk |
| Publisher | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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