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COntroller adaptive Digital Assistant

Project description

DEENESFRITPL

Digital assistant for workplace optimisation

AI technologies have garnered significantly more attention and use in recent years. Innovative technologies are regularly introduced, highlighting numerous benefits, including enhanced automation, efficiency and quality. However, there is a notable gap in research and examination regarding the cooperation and collaboration between AI-based tools and human workers. This gap has raised concerns about potential future changes in the workplace. In this context, the EU-funded CODA project will develop an innovative system designed to enhance collaboration and efficiency within hybrid human–machine teams. This system will leverage state-of-the-art prediction models, neurophysiological assessments of operators and AI-based adaptability systems. Its primary goal is to optimise the allocation and timing of tasks and workloads, ultimately enhancing workplace efficiency.

Objective

COntroller adaptive Digital Assistant
The CODA project involves developing a system in which hybrid human-machine teams collaboratively perform tasks. To do so, the system put together state of art from different fields: i) Prediction models to foresee future situations and have the system know which activities will be carried out by the operators and their impact on the same human performance; ii) Neurophysiological assessment of mental states to enable the system to know operators’ real current level of workload, attention, stress, fatigue, and vigilance by validating the predicted cognitive models and maximising the effectiveness of the interaction between the human and the machine by developing an HMPE (Human Machine Performance Envelope); iii) AI-based adaptable and explainable systems, to have the system act to prevent future performance or safety issues.
Specifically, the project will show how a system could adapt to specific situations and react accordingly by using advanced adaptable and adaptive automation principles that will dynamically guide the allocation of tasks. The system will assess the operator's cognitive status, use current traffic data to foresee the future tasks that the operator will need to perform in the future, and calculate the impact of those tasks in terms of cognitive complexity. With this information, the system will predict the future mental state of the operator and will act accordingly by developing an adaptive automation strategy. For example, imagine an ATCO managing a complex traffic situation and experiencing a medium workload. The system is aware of this (thanks to the neurophysiological assessment). It predicts that the additional upcoming tasks the ATCO will need to take care of will increase their workload, exceeding the maximum an operator can handle. To avoid this, the system decides how to act, following an adaptation strategy: it may, for instance, increment the level of automation, enable additional AI-based tools, or request a sector splitting.

Fields of science

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Programme(s)

Topic(s)

Call for proposal

HORIZON-SESAR-2022-DES-ER-01

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Funding Scheme

HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

Coordinator

DEEP BLUE SRL
Net EU contribution
€ 397 500,00
Address
VIA DANIELE MANIN 53
00185 Roma
Italy

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Yes
Region
Centro (IT) Lazio Roma
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost
€ 397 500,00

Participants (8)

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Last update: 25 July 2023

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Permalink: https://cordis.europa.eu/project/id/101114765

European Union, 2024