Story ID: 
Dassault Renews Human-machine Interface Research
By: Kerry Lynch
Caption: Dassault Aviation renewed its partnership with research institution ISAE-SUPAERO and its foundation on optimizing human-machine interaction.
Word Count: 308
Dassault Aviation is renewing its collaboration with research institution ISAE-SUPAERO and its foundation on optimizing human-machine interaction. The organizations extended the partnership on the “Design and Architecture of Cognitive Air Systems” initiative that began in 2016.

The initiative involves research on neuro-ergonomics, automated decision-making, and systems engineering to explore different aspects of human-machine collaboration. “The idea is to make civil and military aviation operations more robust, efficient, and safer while guaranteeing complete crew control,” the organizations said.

Dassault expects to integrate lessons-learned tools and techniques developed through this research in its civil and military aircraft within the next decade, the company said.

ISAE-SUPAERO’s Department of Aerospace Vehicles Design and Control (DCAS) is leveraging its expertise in neuro-ergonomics and AI for systems control. This includes evaluating the mental state of users as they complete tasks, developing algorithms for automated decision-making, and ensuring the AI works in concert with crew management.

“Dassault Aviation is particularly interested in issues surrounding human-machine interaction because military aviation is a very demanding field due to the variety and unpredictability of missions that require complex tactical management,” said Jean-Louis Gueneau, Dassault Aviation’s scientific coordinator involved with the initiative. “The challenge lies in providing humans with all the services they need to take responsibility for this management. This is why we are working with ISAE-SUPAERO to identify the phenomena that impact collaboration between crews and their machines.”

The first five years of the initiative involved the development of tools based on physiological measurements, machine learning techniques, and automated action planning. This included developing active or passive assistance functions to help pilots improve their performance.

Research initially centered on pilot monitoring to gain a better understanding of a crew’s activity and included experiments using simulators to develop behavioral and physiological metrics.