In the Horizon 2020 funded Future Sky Safety programme, the Human Performance Envelope project pushed airline pilots to the edges of their performance in real-time cockpit simulations, by increasing stress and workload, and decreasing situation awareness. The aim was to find out how such factors interact, and to detect the edges of human performance where some form of automation support should be employed to ensure safe continued flight. A battery of measures was used, from behavioural to physiological (e.g. heart rate, eye tracking and pupil dilation), to monitoring pilot performance in real time. Several measures – e.g. heart rate, heart rate variability, eye tracking, cognitive walkthrough, and Human Machine Interface (HMI) usability analysis – proved to be useful and relatively robust in detecting performance degradation, and determining where changes in information presentation are required to better support pilot performance in challenging situations. These results led to proposed changes in a prototype future cockpit human-machine interface, which were subsequently validated in a final simulation. The results also informed the development of a ‘Smart-Vest’ that can be worn by pilots to monitor a range of signals linked to performance.

An event-related potential (ERP)-based brain–computer interface (BCI) can be used to monitor a user’s cognitive state during a surveillance task in a situational awareness context. The present study explores the use of an ERP-BCI for detecting new planes in an air traffic controller (ATC). Two experiments were conducted to evaluate the impact of different visual factors on target detection. Experiment 1 validated the type of stimulus used and the effect of not knowing its appearance location in an ERP-BCI scenario. Experiment 2 evaluated the effect of the size of the target stimulus appearance area and the stimulus salience in an ATC scenario. The main results demonstrate that the size of the plane appearance area had a negative impact on the detection performance and on the amplitude of the P300 component. Future studies should address this issue to improve the performance of an ATC in stimulus detection using an ERP-BCI.

Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for implementing a visual brain–computer interface based on event-related potentials (ERP-BCI) for patients with limited ocular motility. This paradigm presents stimuli centered in the user’s field of vision, which may hinder the patient from attending to other elements on the screen. A potential solution to this could be the use of semi-transparent stimuli, allowing both the stimulus and the background to be perceived. Therefore, the objective of this study is to evaluate the impact of stimulus transparency on ERP-BCI performance under the RSVP paradigm. Five participants tested the ERP-BCI under RSVP using three different stimulus transparencies with alpha channels set to 255 (C1), 85 (C2), and 28 (C3). The results showed the following average BCI classification accuracies: C1, 70%; C2, 74.67%; and C3, 60.67%. Although the analyses did not reveal significant differences, the results suggest that the transparency level should be carefully manipulated to maintain a balance between stimulus transparency and performance, with C2 even exhibiting the highest accuracy among the conditions. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.

The aim of this paper is to explore the potential challenges associated with contextualizing handover instructions between different stakeholders in the context of a remotely piloted aircraft. The difficulty of synthesizing pilots' exchanges has already been highlighted. In this study, the operational framework focuses on both the construction of representations and narrative discourse to synthesize the situation. To conduct this study, subjects participated in an experiment in which they were asked to place themselves in a handover situation as an incoming and as an outgoing party. This study has made it possible to identify different strategies for constructing the representation of the situation and for transmitting instructions to contextualize an operation. Using an information gathering analysis method, it is possible to assess not only the relevant elements of the context, but also the narrative progression of these elements in the discourse. This is part of the development of an automated assistant that mediates the sequence and, more generally, the contextualization between two human operators or between a human operator and an artificial agent. Those primary results are the first step towards appropriate standardized protocols and checklists, the implementation of technological solutions such as automation and digital communication systems to improve situational awareness and communication skills. The aim is to identify and apply generic strategies that can be used as a framework for the automated development of effective handover protocols in future UAMs, piloted or remoted-piloted aircraft and other flight operations, contributing to safer and more efficient flight operations.

L’objectif de cette thèse est une étude des processus de prise de décision en environnement dynamique complexe et temps contraint, appliquée à l’aéronautique (pilotes d’aviation civile).Nous avons débuté notre approche par une analyse élargie de la littérature. Ce travail nous a permis de : préciser les spécificités liées aux conditions environnementales (incertitude, risque, résilience) ; étudier les capacités cognitives mises en jeu dans le cadre d’une situation dynamique (attention, mémoire, ressources cognitives et modélisation, planification) ; définir les processus de prise de décision (conscience de la situation, représentation mentale, théorie de l’esprit).Nous avons abouti à la conclusion que la prise de décision s’appuie sur une représentation mentale intériorisée du monde. Cette représentation repose sur des expériences passées et leur structuration dans la mémoire, de manière explicitée mais aussi inconsciente.À partir de ces résultats, nous avons donc cherché à mettre en œuvre une méthode permettant d’analyser à la fois ces éléments conscients de la représentation mentale de la situation pour les pilotes d’aéronefs, mais également d’en explorer les aspects tacites.L’étude de la littérature, ainsi que notre expérience issue d’études antérieures ou connexes, nous ont dirigés vers les méthodes issues de la psychologie, comme les entretiens d’auto-confrontation. C’est sur cette base que nous avons conçu une méthode d’analyse de la représentation mentale dédiée aux problématiques de l’interaction homme-système. Cette méthode baptisée MERIA, pour Mental Representation Impact Analysis, permet de réaliser une chronique de l’activité décisionnelle donnant notamment accès aux aspects implicites de la prise de décision.Cette méthode a été éprouvée sur un panel de 30 pilotes de ligne et 3 types d’interfaces de cockpit d’avion de ligne (base A320). Ces expérimentations ont été réalisées en deux séries expérimentales dans le cadre du projet européen Horizon 2020 « Future Sky Safety – Human Performance Envelope », et se poursuivent dans le cadre du projet CO2TEAM (Cleansky2). Le but de ces projets est de réaliser une évaluation et une modélisation des différents paramètres qui entrent en compte dans la mesure de la performance d’un opérateur en interaction avec un système.Grâce à la méthodologie MERIA, nous avons pu valider que certaines interfaces ou modalités d’interaction favorisaient des représentations mentales plus pertinentes/performantes au regard des situations d’usage.Le présent travail s'appuie sur les travaux réalisés dans le cadre du programme Future Sky Safety, financé par le programme de recherche et d'innovation Horizon 2020 de l'Union Européenne au titre de l'accord de subvention n° 640597.

OLIVIA (OperationaL Intentions adVIser for Aviation) was developed in the HAIKU project. It is a flight deck tool providing support to mission-level decisions in complex situations by assessing and prioritizing route options according to operational intentions. It uses Artificial Intelligence to translate (1) operational intentions from pilots to route generation and optimization inputs and (2) route proposal KPIs into operational intention assessments. This paper reports on the final development of OLIVIA, the results from the human-in-the-loop experiments, and insights and recommendations regarding the development of similar assistants for the flight deck.