The SESAR 3 JU has identified the following innovative research elements that could be used to meet the challenge described above and achieve the expected outcomes.

• AI for higher automation. This element covers the development of an AI-powered infrastructure and services (supporting higher levels of automation). In addition, the aim is to develop automation of ATM processes in which analysis and prediction are particularly likely to benefit from AI, and to develop AI-powered ATM environment requirements, infrastructure, and common regulation and certification guidelines.
• Exploring underuse AI paradigm in ATM. AI Paradigms (X-axis) are the approaches used by AI researchers to solve specific AI-related problems. Research aims at investigating these alternative possibilities (R&I need: human–AI collaboration: digital assistants).
• Transfer-learning and few-shot learning methodologies in ML ad XAI. Research focuses on transfer-learning and few-shot learning methodologies. In ATM domain, the transfer-learning methodology could be another essential research and development direction for utilizing machine learning and XAI. The lifelong machine can incorporate transfer learning for parameterizing to learn domain-invariant features (e.g., how existing AI models can be used for solving different tasks that share common features or attributes).
• Innovative methodologies for ATM safety, security and resilience. Research aims at developing methodologies (or evolution of existing ones) for safety, security and resilience that will contribute to ensure that ATM is robust against ever-evolving risks, threats and disruptive events in the physical and cyber worlds in an environment with automation levels 4/5.
• Ensuring the integrity of non-ATM data for AI/ML applications in ATM. For artificial intelligence and machine learning applications in aviation the integrity and quality of input data is critical. The research shall address these non-ATM data availability and format, proposing a framework for data curation, sharing and feeding oriented to ATM use cases, as well as developing new indicators at least for data quality and integrity (R&I need: Trustworthy AI-powered ATM environment).
• Enhancing robustness and reliability of machine learning (ML) applications. Research aims at enhancing machine learning (ML) applications to ensure they are technically robust, accurate and reproducible, and able to deal with and inform about possible failures inaccuracies and errors. Research aims at developing potential solutions to address this challenge, which shall include/refer to the EASA methodologies for certification of AI in aviation. The scope may address:
  • Verification methods of robustness for machine learning (ML) applications.
  • Standardised methods for evaluation of the operational performance of the machine learning (ML).
  • Application of transfer learning and data augmentation techniques for the development of the proposed applications, thus guaranteeing their robustness.
  • Identification, detection and mitigation means of bias in ML applications.
• Accelerating AI implementation for ATC automation. The research seeks for environments where full (or close to full) ATC automation may become a reality in the short term without human supervision. Research also addresses exploratory activities on solutions non-dependant of human supervision to take back control to solve contingency is necessary.
• Just culture and AI. State of the art algorithms for AI/ML systems such as neural networks are essentially “black boxes” in terms of explainability.  The introduction of AI/ML in essence clouds the drawing of a red line between “gross negligence”, “wilful violations” and “destructive acts” on the one side and “honest mistakes” on the other side. Research aims at redefining just culture and rewrite its procedures in the era of digitalization (R&I need: Trustworthy AI-powered ATM environment).
• Development of ATM specific ontologies. This research element focuses on special-purpose representation systems (e.g., semantic networks and description logics) that can be devised to help organizing a hierarchy of ATM related categories.