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HUMAN AIRCRAFT ROADMAP FOR VIRTUAL INTELLIGENT SYSTEM

Deliverables

Pilot Training consideration for the implementation of a digital assistant

This deliverable will focus on how pilots should be trained in such a context to make the solution robust and effective

Human-Machine Interface and Envelope

This deliverable will present the task analysis of large commercial aircraft by focusing on the pilot interaction with the systems and the nature of information delivered in each step of the activities

Technologies Roadmap

D43 will present the roadmap of technologies for the next generation digital assistantWork package number

Analysis of Potential Cognitive Computing Aided Tasks

Cockpit operations of large commercial aircraft such as the A350, A380 or A320, or similar, will be analysed along 2 directions: the pilot [“man in the loop”] interaction with the systems and the nature of information delivered. The analysis should allow for identifying specific in-flight conditions, related pilot behaviour, and how data is interpreted, and how and when collaborative / assisted decisions are made. This task will also devise initial case studies deemed relevant to extrapolate the potential of cognitive computing and to demonstrate how they will offer a significant aid to the pilot. The knowledge gained will help developing the initial concept for a digital assistant function supporting full or reduced flight crews.

Validation Plan

This deliverable will include the validation objectives and the workplan for the validation exercises Detailed scenarios metrics and schedule will be generated

State of the Art of cognitive computing algorithms

This deliverable will present a state of the art of cognitive computing applied in cockpit, healthcare and other relevant fields for the project. The state of the art will collect the most relevant papers and projects related to machine learning and artificial intelligence algorithms and the application of these alrogithms. This state of the art will set the basis for the project in order to explore the use cases and produce the roadmap that will be included in later deliverables.

Analysis Report

This deliverable will include the multicriteria analysis performed on the basis of the simulations

Second Demonstrator

This deliverable will present the design of the use case and the result of the study Additional documentation describing the prototype its functionalities the algorithms used and the results of the preliminary tests will be also delivered

First Demonstrator

D31 will present the design of the use case and the result of the study Additional documentation describing the prototype its functionalities the algorithms used and the results of the preliminary tests will be also delivered

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Publications

HARVIS Project AI Assistant for SPO - LPA P3

Author(s): Duchevet, Alexandre; Bejarano Espada, Carmen; Imbert, Jean-Paul; Rodríguez Vázquez, Antonio Leopoldo; Colomer Granero, Adrián; Cantero Ramis, Jesús; Ferreira, Ana; Moens, Laura
Published in: 2021
Publisher: Zenodo
DOI: 10.5281/zenodo.6413977

Aircraft Dynamic Rerouting Support

Author(s): NÚÑEZ SÁNCHEZ, Javier; LEOPOLDO RODRÍGUEZ VÁZQUEZ, Antonio; MOGOLLÓN GARCÍA, Juan Manuel; GRANGER, Géraud; IMBERT, Jean-Paul; DUCHEVET, Alexandre; BONELLI, Stefano; NARANJO ORNEDO , Valery; COLOMER GRANERO, Adrián
Published in: 2020
Publisher: 1st International Conference on Cognitive Aircraft Systems – ICCAS
DOI: 10.5281/zenodo.6414460

Cognitive assistant in the cockpit

Author(s): Rodríguez Vázquez, Antonio Leopoldo; Bonelli, Stefano; Duchevet, Alexandre; Imbert, Jean-Paul; Colomer Granero, Adrián; Cantero, Jesús; Bejarano Espada, Carmen; Ferreira, Ana
Published in: 2020
Publisher: Zenodo
DOI: 10.5281/zenodo.6413853

HARVIS Project AI Assistant for SPO

Author(s): Duchevet, Alexandre; Bejarano Espada, Carmen; Rodríguez Vázquez, Antonio Leopoldo; Imbert, Jean-Paul; Ferreira, Ana; Moens, Laura; Colomer Granero, Adrián; Colomer Granero, Adrián
Published in: 2021
Publisher: Zenodo
DOI: 10.5281/zenodo.6413887

Toward a Non Stabilized Approach assistant based on human expertise

Author(s): DUCHEVET, Alexandre; GRANGER, Géraud; IMBERT, Jean-Paul; DE LA HOGUE, Théo; LEOPOLDO RODRÍGUEZ VÁZQUEZ, Antonio; NÚÑEZ SÁNCHEZ, Javier; MOGOLLÓN GARCÍA, Juan Manuel; BONELLI, Stefano; FERREIRA, Ana; NARANJO ORNEDO , Valery; COLOMER GRANERO, Adrián; JIMENEZ CAMPFENS, Néstor
Published in: 2020
Publisher: 1st International Conference on Cognitive Aircraft Systems – ICCAS
DOI: 10.5281/zenodo.6414367

Presentation of HARVIS Project

Author(s): LEOPOLDO RODRÍGUEZ VÁZQUEZ, Antonio; NÚÑEZ SÁNCHEZ, Javier; DUCHEVET, Alexandre; IMBERT, Jean-Paul; BONELLI, Stefano; FERREIRA, Ana; NARANJO ORNEDO , Valery; COLOMER GRANERO, Adrián
Published in: 2020
Publisher: N/A
DOI: 10.5281/zenodo.6416565

Presentation of HARVIS Project

Author(s): Rodríguez Vázquez, Antonio Leopoldo; Imbert, Jean-Paul; Naranjo Ornedo, Valery; Bonelli, Stefano
Published in: 2019
Publisher: N/A
DOI: 10.5281/zenodo.6416556

Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

Author(s): Néstor Jiménez-Campfens, Adrián Colomer, Javier Núñez, Juan M. Mogollón, Antonio L. Rodríguez, Valery Naranjo
Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II, 12490, 2020, Page(s) 148-155, ISBN 978-3-030-62364-7
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-62365-4_14