GSS EU Science Funding Gateway

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

• Innovative methodologies for quantifying the impact on safety and resilient performance of higher automation in ATM. This research aims at developing new methodologies for quantifying the impact on safety and resilient performance of higher automation in the ATM system applying data-driven techniques.
• Safety filter for AI solution. Study safety filter concept for data prediction responsible for deciding on the usability of the ML model predicted data. Safety filter works as a safeguard, without human intervention, and qualifies the predicted sensor-data, as valid or invalid, by applying captured expertise rules.
• Artificial situational awareness: This research aims at developing artificial situational awareness solutions based on high-integrity information that are able to describe the traffic situation on a sector (en-route / TMA) or airport and, integrated with machine-learning (ML), enable the assessment of probabilistic events (e.g., trajectory prediction or conflict detection).
• Explainable Artificial Intelligence (XAI): The objective of this research is to improve transparency of automated systems in the ATM domain investigating methods based on Explainable Artificial Intelligence (XAI) in operational use cases e.g., predicting air traffic conflict resolution and delay propagation, validating the robustness and transparency of the system, etc.
• AI-powered co-piloting. Research aims at investigating how AI can support pilots in complex and critical situations, when workload may be high and/or the time to react very limited.
• AI for complex operations. This research is about developing AI-based human operator support tools to ensure the safe integration of new entrant aircraft types into an increasingly busy, heterogeneous and complex traffic mix (i.e. unmanned aircraft systems, supersonic aircraft, hybrid and fully electric aircraft).
• User interface providing conflict resolution advisory transparency. This research focuses on visual elements that allow better understanding why a particular conflict resolution solution is recommended.
• Guidelines for the design of future AI systems. This research relates to the application of EASA guidelines to the development of AI enabled systems in ATM.
• Support to the certification of novel ATM-related AI/ML-based airborne and ground systems that enable higher levels of automation. The objective of this research element is to address issues related to the certification of novel ATM-related airborne and ground systems that enable higher levels of automation.
• Development of framework to achieve effective Human-AI Teaming. As AI is developed to provide more intelligent behaviours, it is argued that there will be an increased need for AI systems to function effectively as team members with humans.
• Extended reality (XR) in support of ATM operations. The research would investigate the operational and technical feasibility of training individually or collectively with other ATCOs as if they were executing remote simulations or validations through connection with remote real simulators.

Further details at the Funding&Tenders.