Our paper “Recognizing Pilot State: Enabling Tailored In-Flight Assistance Through Machine Learning” has been published in the proceedings of the 1st International Conference on Cognitive Aircraft Systems:
Lutnyk, L., Rudi, D., Kiefer, P., & Raubal, M. (2020). Recognizing Pilot State: Enabling Tailored In-Flight Assistance Through Machine Learning ICCAS 2020.
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.
The publication is part of PEGGASUS. This project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 821461