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Author Archives: Luis Lutnyk

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Workshop Paper published from ICCAS 2020

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


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Workshop Paper published from ETAVI 2020

Our paper Towards Pilot-Aware Cockpits has been published in the proceedings of the 1st International Workshop on Eye-Tracking in Aviation (ETAVI 2020):

Lutnyk L., Rudi D., and Raubal M. (2020). Towards Pilot-​Aware Cockpits. In Proceedings of the 1st International Workshop on Eye-​Tracking in Aviation (ETAVI 2020), ETH Zurich. DOI: https://doi.org/10.3929/ethz-b-000407661

Abstract. Eye tracking has a longstanding history in aviation research. Amongst others it has been employed to bring pilots back “in the loop”, i.e., create a better awareness of the flight situation. Interestingly, there exists only little research in this context that evaluates the application of machine learning algorithms to model pilots’ understanding of the aircraft’s state and their situation awareness. Machine learning models could be trained to differentiate between normal and abnormal patterns with regard to pilots’ eye movements, control inputs, and data from other psychophysiological sensors, such as heart rate or blood pressure. Moreover, when the system recognizes an abnormal pattern, it could provide situation specific assistance to bring pilots back in the loop. This paper discusses when pilots benefit from such a pilot-aware system, and explores the technical and user oriented requirements for implementing this system.

Edit. 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


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PEGGASUS in the news

Our research project PEGGASUS (Pilot Eye Gaze and Gesture tracking for Avionics Systems using Unobtrusive Solutions) has attracted quite a bit of coverage in the media.

See for yourself in this little press review:

Edit. 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


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New aviation project: PEGGASUS

PEGGASUS (Pilot Eye Gaze and Gesture tracking for Avionics Systems using Unobtrusive Solutions)

We’re glad to announce the start of a new aviation project at the GeoGazeLab.

Check out our vision for pilot interactions in the cockpit of the future at the project page.

Edit. 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