Project description
Trustworthy and transparent AI for remote digital towers
Remote digital towers (RDTs), an innovative solution for enhancing data management at airports, are increasingly being adopted by airports worldwide to enhance safety and efficiency. Nevertheless, the substantial volume of generated data demands significant effort and attention for effective utilisation and organisation. The EU-funded TRUSTY project aims to enhance RDTs by leveraging artificial intelligence (AI) to boost their efficiency, data capacity and overall durability. Additionally, it aims to ensure higher levels of transparency and trustworthiness in their AI systems. To accomplish these objectives, the project will harness innovative interactive data visualisation discoveries, recent technologies and information visualisation techniques.
Objective
Remote digital towers (RDT) are taking place around the world to ensure efficiency and safety. TRUSTY harnesses the power of artificial intelligence (AI) to enhance resilience, capacity, and efficiency in making significant advancements in the deployment of digital towers. The overall goal of TRUSTY is to provide adaptation in the level of transparency and explanation to enhance the trustworthiness of AI-powered decisions in the context of RDT. Through the video transmission data from RDT, TRUSTY considers the following major tasks:
1. Taxiway monitoring (i.e. bird hazard, presence of a drone, autonomous vehicle monitoring, human intrusion, etc.).
2. Runway monitoring (approach and landing) misalignment warning and the corresponding explanation.
To deliver trustworthiness in an AI-powered intelligent system several approaches are considered:
• ‘Self-explainable and Self-learning’ system for critical decision-making
• ‘Transparent ML’ models incorporating interpretability, fairness, and accountability
• ‘Interactive data visualization and HMI dashboard’ for smart and efficient decision support
• ‘Adaptive level of explanation’ regarding the user's cognitive state.
• “Human-centric AI” enhances the trustworthiness of AI-powered systems.
• “Human-AI teaming” to consider users’ feedback to insure some computation flexibility and the users’ acceptability.
To achieve the goal, TRUSTY will rely on the SotA developments in interactive data visualization, and user-centric explanation and on recent technological improvements in accuracy, robustness, interpretability, fairness, and accountability. We will apply information visualization techniques like visual analytics, data-driven storytelling, and immersive analytics in human-machine interactions (HMI). Thus, this project is at the crossroad of trustworthy AI, multi-model machine learning, active learning, and UX for human and AI model interaction.
Fields of science
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsdrones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- social scienceseducational sciencespedagogyactive learning
Programme(s)
- HORIZON.2.5 - Climate, Energy and Mobility Main Programme
Funding Scheme
HORIZON-JU-RIA - HORIZON JU Research and Innovation ActionsCoordinator
721 23 VASTERAAS
Sweden