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Communication dans un congrès

Machine learning for drone operations: challenge accepted

Elgiz Baskaya 1, 2 Murat Bronz 1 
2 ENGIE Ineo - Safran RPAS Chair
SAFRAN Group, Aéroports de Paris, ENAC - Ecole Nationale de l'Aviation Civile
Abstract : Machine learning is among the top research topics of the last decade in terms of practicality and popularity. Though often unnoticed, machine learning guides many aspects of our lives since its introduction via the big tech companies. Its abilities rise, defeating 9-dan Go professional, their accuracy increase, enabling smooth voice recognition, adding intelligence to our daily lives. However, its development is mostly supported by high tech companies for now rather than the public, or regulations, who show increasing concern about its usage. Despite some reluctance, machine learning has started to appear in aviation as well. Operational improvements were among the first applications. In this paper, we offer to present an introduction to machine learning, compare it with well known modeling techniques by giving an example from aviation and question their fitness for certification. We discuss the enablers and try to understand the limitations that might result or prevent the use of machine learning on certified safety systems. Similar considerations are held for systems that do not require certification, but need to be taken into account in risk analysis methods. The ultimate purpose of this paper is to highlight the existing challenges which prevent machine learning algorithms from having a wider role in drone avionics, and more generally in aviation.
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Communication dans un congrès
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Soumis le : samedi 21 novembre 2020 - 22:14:14
Dernière modification le : mercredi 3 novembre 2021 - 08:17:33




Elgiz Baskaya, Murat Bronz. Machine learning for drone operations: challenge accepted. DASC 2020 AIAA/IEEE 39th Digital Avionics Systems Conference, Oct 2020, San Antonio, United States. pp.ISBN:978-1-7281-8088-5, ⟨10.1109/DASC50938.2020.9256557⟩. ⟨hal-03018054⟩



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