TAPAS (Towards an Automated and exPlainable ATM System) addresses the effectiveness of introducing AI/ML solutions in order to increase the levels of automation in ATM, considering the need of the operator to trust the system (taken as the ability to understand and explain its behaviour and outcomes).
The main objective for the project was the exploration of highly automated XAI scenarios through validation activities and Visual Analytics (VA), in order to identify needs and strategies to address transparency and explainability in the operational cases considered, paving the way for the application of these AI/ML technologies in ATM environments, in particular in automation levels 2 and 3 as expressed in the successive editions of the European ATM Master Plan.
This is reflected in two key technical objectives:
Objective 1: Identification of principles and criteria for AI/ML transparency/explainability in ATM domain scenarios
This objective applies to the two operational cases considered (ATFCM – Air Traffic Flow and Capacity Management; and CD&R – Conflict Detection and Resolution) and with the target to identify transparency requirements for AI/ML methods in general, limiting domain-specific results. The strategy to achieve this goal was based in addressing different temporal, functional and safety-critical perspectives, as those provided by the complementary operational cases considered in TAPAS. The project put specific focus to maximise the applicability of results to different operational environments, while setting the limitations when this is not feasible.
The project explored the use of XAI and VA to apply them in the operational cases considered, through practical experiments and validation activities in simulation platforms. In particular, for each level of automation considered in each operational case, the project implemented a distribution of functionalities between the human and the machine including AI/ML ones. These were verified using real-time simulations (RTS) including operational staff (Air Traffic Controllers) both providing a-priori and a-posteriori expert judgement, and objective criteria verification.
Objetive 2: Selection and development of suitable and explainable AI/ML methods in the operational cases identified
This selection had the goal to fit the needs of transparency as expressed in the explainability criteria developed for each automation level and according to actors’ needs.
The project developed prototypes of XAI methods which addressed the balance between explainability and effectiveness according to specific needs, but also in search of developing a more general taxonomy of AI/ML techniques considering the two aforementioned magnitudes. Given the early Technology Readiness Level (TRL) of this project (initially pre-TRL 1, although eventually the project reached a "TRL 2 ongoing" classification), these prototypes were focused on testing purposes.
TAPAS project achieved both objectives, also reaching fully TRL 1 maturity and partly TRL 2.
