Description du projet
Intelligence artificielle et transparence dans la gestion du trafic aérien
On attend beaucoup des technologies d’intelligence artificielle (IA) et d’apprentissage automatique (ML pour machine learning) qui apportent une évolution majeure à la gestion du trafic aérien (ATM), avec un système hautement automatisé capable de fournir une plus grande capacité. La fiabilité et la sécurité de ces systèmes restent néanmoins une question cruciale pour les utilisateurs et les opérateurs et constituent un obstacle fondamental à l’adoption des technologies d’IA/ML dans tous les domaines. L’objectif principal du projet TAPAS, financé par l’UE, est de fournir un ensemble de principes et de critères qui ouvrent la voie au déploiement de ces technologies dans l’ATM de manière sûre et fiable. Les techniques d’intelligence artificielle explicables (XAI), associées à l’analyse visuelle, permettront de déterminer des compromis entre l’efficacité des mises en œuvre de l’IA et l’opportunité de son déploiement dans des applications spécifiques.
Objectif
As Artificial Intelligence (AI) becomes an increasing part of our lives in general, individuals are finding that the need to trust these AI based systems is paramount. Air Traffic Management (ATM) is not an stranger to this: with a system close to, or already at, a saturation level, AI applications are considered a main enabler to reach higher levels of automation.
This would mean a fundamental shift in the automation approach when moving from the classical human-machine interaction to a potentially much richer solution enabled by these AI systems, in which trust in the operations needs to be generated. As humans, operators must be able to fully understand how decisions are being made so that they can trust the decisions of AI systems. The lack of explainability and trust hampers the ability (both individual and global) to fully trust AI systems.
TAPAS aims at exploring highly automated AI-based scenarios through analysis and experimental activities applying eXplainable Artificial Intelligence (XAI) and Visual Analytics, in order to derive general principles of transparency which pave the way for the application of these AI technologies in ATM environments, enabling higher levels of automation.
Specifically, TAPAS will:
• Analyse two operational environments: ATC (Air Traffir Control)Conflict Detection & Resolution (tactical), and Air Traffic Flow Management (pre-tactical). For them, levels of automation 1 to 3 according to SESAR Model will be considered.
• Develop eXplainable Artificial Intelligence (XAI) prototypes addressing the requirements and acceptability criteria of the scenarios.
• Run experiments that assess the applicability of these XAI modules in the higher levels of automation considered, exploring different ways of interaction and information exchange.
• Apply Visual Analytics techniques to contribute to explainability of decissions.
• Extract conclusions, principles and recommendations related to transparency of AI in ATM.
Champ scientifique
Programme(s)
Régime de financement
RIA - Research and Innovation actionCoordinateur
28022 Madrid
Espagne