Skip to main content
Go to the home page of the European Commission (opens in new window)
English English
CORDIS - EU research results
CORDIS

Modern ATM via Human/Automation Learning Optimisation

Project description

A smart new system to support air traffic controllers

Increased AI and machine learning (ML) automation enable better performance, output, efficiency in solving problems, safety and control of processes. But technology that replaces human activity could create problems when its processes are not understandable for humans. The EU-funded MAHALO project aims to design an automated AI, ML and deep neuronal learning-based explainable system for problem solving between aircrews and air traffic controllers. Trained by the individual operator, the machine will be able to inform the operator what it has learnt. This will increase capacity, performance and safety. Specifically, MAHALO will investigate the impact of transparency (how much the AI is able to explain why it took a specific decision) and conformity (how much the decision taken by the AI is similar to the one a controller would choose). The project will be evaluated in real-time simulations for traffic difficulties, trust, acceptance and controller understanding. MAHALO’s framework will serve as a model for future AI systems.

Objective

MAHALO asks a simple but profound question: in the emerging age of Machine Learning (ML), should we be developing automation that matches human behavior (i.e. conformal), or automation that is understandable to the human (i.e. transparent)? Further, what tradeoffs exist, in terms of controller trust, acceptance, and performance? To answer these questions, MAHALO will:
• Develop an individually-tuned ML system comprised of layered deep learning and reinforcement models, trained on controller performance (context-specific solutions), strategies (eye tracking), and physiological data, which learns to solve ATC conflicts;
• Couple this to an enhanced en-route CD&R prototype display to present machine rationale with regards to ML output;
• Evaluate in realtime simulations the relative impact of ML conformance, transparency, and traffic complexity, on controller understanding, trust, acceptance, workload, and performance; and
• Define a framework to guide design of future AI systems, including guidance on the effects of conformance, transparency, complexity, and non-nominal conditions.
Building on the collective experience within the team, past research, and recent advances in the areas of ML and ecological interface design (EID), MAHALO will take a bold step forward: to create a system that learns from the individual operator, but also provides the operator insight into what the machine has learnt. Several models will be trained and evaluated to reflect a continuum from individually-matched to group-average. Most recent work in areas of automation transparency, Explainable AI (XAI) and ML interpretability will be explored to afford understanding of ML advisories. The user interface will present ML outputs, in terms of: current and future (what-if) traffic patterns; intended resolution maneuvers; and rule-based rationale. The project’s output will add knowledge and design principles on how AI and transparency can be used to improve ATM performance, capacity, and safety.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

You need to log in or register to use this function

Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

RIA - Research and Innovation action

See all projects funded under this funding scheme

Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-SESAR-2019-2

See all projects funded under this call

Coordinator

DEEP BLUE SRL
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 193 125,00
Address
VIA DANIELE MANIN 53
00185 ROMA
Italy

See on map

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

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 193 125,00

Participants (4)

My booklet 0 0