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Recognizing Pilot State: Enabling Tailored In-Flight Assistance Through Machine Learning

Lutnyk, Luis and Rudi, David and Kiefer, Peter and Raubal, Martin Recognizing Pilot State: Enabling Tailored In-Flight Assistance Through Machine Learning. (2020) In: 1st International Conference on Cognitive Aircraft Systems - ICCAS 2020, 18 March 2020 - 19 March 2020 (Toulouse, France). (Unpublished)

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Abstract

Motivation: Moving towards the highly controversial single pilot cockpit, more and more automation capabilities are added to today’s airliners 1. However, to operate safely without a pilot monitoring, avionics systems in future cockpits will have to be able to intelligently assist the remaining pilot. One critical enabler for proper assistance is a reliable classification of the pilot’s state, both in normal conditions and more critically in abnormal situations like an equipment failure. Only with a good assessment of the pilot’s state, the cockpit can adapt to the pilot’s current needs, i.e. alert, adapt displays, take over tasks, monitor procedures, etc. [2].

Item Type:Invited Conference
Audience (conference):International conference without published proceedings
Uncontrolled Keywords:
Institution:Other partners > Eidgenössische Technische Hochschule Zürich - ETHZ (SWITZERLAND)
Statistics:download
Deposited On:10 May 2021 14:26

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