Recognizing Pilot State: Enabling Tailored In-Flight Assistance Through Machine Learning
Open access
Date
2020-03Type
- Other Conference Item
ETH Bibliography
yes
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Abstract
Moving towards the highly controversial single pilot cockpit, more and more automation capabilities are added to today’s airliners. 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000454675Publication status
publishedBook title
The 1st International Conference on Cognitive Aircraft Systems – ICCASPages / Article No.
Publisher
ISAE-SUPAERO, Université de Toulouse; Institute of Cartography and Geoinformation (IKG), ETH ZurichEvent
Organisational unit
03901 - Raubal, Martin / Raubal, Martin
Funding
821461 - Pilot Eye Gaze and Gesture tracking for Avionics Systems using Unobtrusive Solutions (EC)
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually. Project H2020 "Clean Sky 2 JU".More
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ETH Bibliography
yes
Altmetrics