Description :
ExpectedOutcome:
Project results are expected to contribute to the following expected outcomes.
- Environment. AI will enable the optimisation of aircraft trajectories, potentially reducing the aviation environmental footprint.
- Capacity. AI will play a fundamental role in aviation/ATM to address airspace capacity shortages, enabling dynamic configuration of the airspace and allowing dynamic spacing separation between aircraft.
- Cost-efficiency. AI will enrich aviation datasets with new types of datasets, unlocking air–ground cooperation using AI-based applications, fostering data-sharing and building up an inclusive AI aviation–ATM partnership. This will support decision-makers, pilots, ATCOs and other stakeholders, bringing benefits in cost-efficiency by increasing ATCO productivity (reducing workload and increasing complexity capabilities).
- Operational efficiency. Increasing predictability will be a key function of AI, as it will enable traffic predictions and forecasts that will boost punctuality.
- Safety. Safety science will also need to evolve to cope with the safety challenges posed by the introduction of ML. Current safety levels will be at least maintained using this technology.
- Security. AI will make it possible to stay cyber-resilient in the face of new technologies and threats; the objective is to maintain a high level of security.
Scope:
To achieve the expected outcomes, all or some of the following should be addressed.
- Trustworthy AI-powered ATM environment. This refers to the the development of advanced AI applications (e.g. supporting automation level 4) for ground or airborne use, with a particular focus on the demonstration of new methodologies for the validation and certification of advanced AI applications that will ensure their transparency, robustness and stability under all conditions. It includes aspects such as explainability, learning assurance, formal methods, testing, licensing, in-service experience and online learning assurance (R&I need: trustworthy AI-powered ATM environment).
- AI for prescriptive aviation. This refers to the development of digital solutions and services leveraging state-of-the-art technologies to demonstrate how AI can be used in a highly automated and safety-critical environment to deliver substantial and verifiable performance benefits while at the same time fully addressing safety concerns and using human skills. It also includes, for example, abnormal situation management. AI/ML have great potential for predictions/forecasts under normal circumstances, but further evolution will be needed if they are to be used in the management of abnormal situations: a prescriptive approach will be required to monitor reality and specify precursors indicating possible deviations from what is expected. This covers the exploitation of aviation data hubs. Developments in this area might include, for example, solutions for the detection of abnormal situations and aircraft behaviour (i.e. deviations from what is expected); ML, big data and predictive analysis techniques will make it possible to analyse situations, predict potential aircraft trajectories and detect suspicious aircraft (R&I need: AI for prescriptive aviation)
- Human–AI collaboration. This element will involve the development of digital solutions leveraging state-of-the-art technologies to support aviation actors in a highly automated environment (automation level 4) while ensuring that humans understand what the systems are doing and maintain the right level of situational awareness (R&I need: human–AI collaboration: digital assistants). It includes for example,:
- advanced AI applications for airlines, ANSPs and airport managers in a range of areas such as fleet management, infrastructure monitoring, sectorisation and staff planning;
- advanced AI applications for regulators, with new safety and security indicators that support the (early) detection and predictions of new risks;
- new HMIs for ATCOs (e.g. augmented reality) and the capability to monitor ATCO workload in real time based on AI, as well as new skills and new training methods to support these new joint human–machine systems.
