Air Traffic Management technological challenges contest
The best solution will be awarded with 60.000 €.
The contest returns in 2025
What is the Air Traffic Management technological challenges contest?
ENAIRE launches this year the 3rd edition of the Air Traffic Management technological challenges contest. The contest is open to university research groups or any other type of group.
We accept technological solutions applicable to any sector of activity, as long as they are focused on the provision of air traffic/air transport services.
Challenges
We are looking for total or partial solutions to any of the following 6 challenges.
Intelligent air binomial: development of a proactive digital assistant for air traffic controllers
The challenge focuses on the development of a digital assistant dedicated to air traffic controllers, whose main function is the proactive management of aircraft separation. We are looking for a system that, from the beginning of its training, learns and understands how controllers deal with the complexities of air traffic control routines and practices.
The assistant should accompany the controller on a daily basis, from the beginning of their training, offering customized options for resolving air separation conflicts based on their common learning and the specific style of the air traffic controller. In the last phase of training, the assistant is expected to autonomously propose and resolve potential conflicts, always under the controller’s supervision.
The challenge drives the creation of a highly personalized Human-Assistant pairing, specialized in the efficient and safe management of aircraft separation. The assistant’s ability to anticipate and resolve separation losses, thus optimizing safety and efficiency in the airspace, will be especially valued.
The digital assistant would be able to identify when the controller’s execution of tasks degrades and therefore refresher training is necessary.
The development of a digital assistant for air traffic controllers involves considering various aspects and challenges specific to this critical environment. It will be positively considered to explain which technology or combination of technologies is the most suitable to address the different challenges (generative AI, deep learning, optimization algorithms, Natural Language Processing (NLP), others).
Exploring the quantum frontier: redefining efficiency in European airspace resource planning
This challenge focuses on the revolutionary application of quantum computing to improve the efficiency and accuracy of resource planning in European airspace, where thousands of aircraft move daily. The goal is to explore how quantum computing can transform the ability to analyze multiple operational scenarios simultaneously and efficiently.
Currently, uncertainty in factors such as inaccurate weather events or takeoff delays lead to conservative planning, with oversized systems and buffers limiting airspace capacity.
Participants must devise quantum solutions that leverage the ability to simulate infinite scenarios to accurately forecast future events. The goal is to eliminate inefficiencies arising from the lack of certainty in operational events and enable more accurate and adaptive resource planning.
Creativity and efficiency in the application of quantum computing to address this problem will be valued, thus offering an innovative approach that could transform the way resources are planned and managed in air navigation control in Europe.
Proactive prevention of air incidents
The challenge is to develop an advanced technological solution to anticipate and prevent air traffic incidents in the field of Air Traffic Management (ATM). Unlike traditional approaches that focus on detecting a single causal factor, the objective is to interrupt the error chain by identifying and correcting any intermediate errors before they trigger an incident.
Participants must propose an intelligent system that, based on available information, actively analyzes operations and detects patterns of anomalous behavior. The solution must be able to identify possible intermediate errors in the chain before they evolve into critical situations.
The effectiveness of the system in anticipating and preventing incidents will be assessed, as well as its ability to integrate harmoniously into the ATM environment. The proposed solution must be proactive, adaptive and capable of working in real time to significantly improve airspace safety and reduce the possibility of air incidents.
Optimization and efficient certification of artificial intelligence (AI) models in air traffic management
In the field of Air Traffic Management (ATM), one of the most significant challenges is how to certify and keep Artificial Intelligence (AI) models up to date in an efficient and safe manner, according to the European Union Aviation Safety Agency (EASA) regulations.
In particular, according to EASA, adaptive learning processes, so commonly used in social networks, where AI systems adjust their behavior and response based on the experience and feedback received by the user in real time, present a greater challenge in such a critical environment as air traffic control. Certification involves demonstrating that such a trained system is predictable, understandable and capable of operating safely in a variety of situations.
To this end, solutions to the challenge could focus on designing strategies so that AI models can always be updated within the limits of their original “Operational Design Domain” (ODD). This means that models must be able to adapt to new conditions or data without going outside the parameters for which they were initially designed and certified. This requires a balance between continuous model improvement and compliance with aviation safety regulations.
The challenge is to find solutions in critical environments other than ATM that can adapt and keep AI models in the aviation domain safe, reliable and up to date, while respecting current regulations and ensuring safety at all times.
Predictive modeling for behaviors in ATM environments without precedents or historical data
This challenge focuses on exploring artificial intelligence in situations without historical data, common in the field of air navigation control. Unlike conventional models that rely on historical data, the objective is to investigate how to use artificial intelligence to predict behaviors in "new" scenarios, where no prior data is available for conventional model training.
Participants should propose innovative approaches for building predictive models that can adapt to significant changes in the air navigation system, even when there is no relevant data history, such as the use of reinforcement and simulation learning, generative models or transfer of learning in related situations. The ability to simulate and identify effects prior to implementation of system changes will be critical to the success of the proposed solutions.
An example occurred during the pandemic where a similar situation had not occurred previously and predicting traffic demand was needed for proper planning of control resources.
Creativity in the application of artificial intelligence techniques in the prediction of behavior in non-historical situations will be especially valued, thus offering advanced tools for air navigation control in dynamic and changing environments.
Innovation for sustainable air traffic
The challenge poses the fundamental question: Can we innovate more in air traffic management to reduce the environmental impact of aviation? Despite current advances, such as the implementation of satellite navigation systems and trajectory optimization algorithms, the challenge is to inspire new ideas that go beyond existing practices.
Currently, strategies in air traffic management include the adoption of advanced technologies such as RNAV/RNP for more efficient routing and continuous trajectory optimization algorithms for real-time adjustments. The introduction of Free Route Airspace, the modernization of ATM systems or collaborative flight planning systems also contribute to overall system efficiency. However, the challenge is to explore bolder and more creative approaches that can radically transform air traffic management and further reduce its environmental footprint.
Participants are invited to propose innovations in air traffic management that consider environmental, economic and operational factors, encouraging collaboration between airlines and controllers to optimize routes and processes. The goal is to drive disruptive ideas that not only optimize air traffic management, but also boost the sustainability and environmental efficiency of air transport into the future.
Phases of the contest
April 30, 2024
1. Submission of proposed solutions
May 22, 2024
May 22, 2023
2. Evaluation of proposed solutions
The Selection Committee will select at least 3 finalists.
May 29, 2024
3. Presentation of the finalist proposals
Finalists will present their proposed solutions before the Jury.
June 12, 2024
4. Selection, notification and acceptance of the challenge
The Jury will notify the winning research group.
July 2024 – December 2025
5. Investigation phase of the winning solution and follow up
From the date of acceptance of the challenge, the winning research group will sign a research agreement to begin the work, which will last 18 months.
Award
The winning research group will sign a research agreement for a period of 18 months and will have at its disposal an amount of 60.000 €, of which 40.000 € will be delivered in 2024 as an early installment and 20.000 € in 2025 after the end of the activities.
The objective is to develop the proposed solution to demonstrate its suitability and technical and economic feasibility.
18 months
Research agreement
60.000 €
In 2 phases
18 months
Research agreement
60.000 €
In 2 phases
¿What is ENAIRE?
In Spain, all aircraft that take off, land or transit through its airspace receive communications, navigation and surveillance services through a modern and complete network of facilities operated by ENAIRE.
In the following infographic they show how air traffic control services are provided, according to the phases of a flight.
Some of Enaire's areas of interest
Safe and smooth air traffic
Environmentally sustainable air traffic
Drone traffic integration
Airspace design and organization
Traffic demand and capacity balancing
Automation
Artificial intelligence
