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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

Authors:

Augustin Degas , Mir Riyanul Islam, Christophe Hurter , Shaibal Barua, Hamidur Rahman, Minesh Poudel , Daniele Ruscio , Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli , Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Fabio Babiloni , Pietro Aricò

Publication Type:

Journal article

Venue:

Applied Sciences - Computing and Artificial Intelligence (Special Issue: Explainable Artificial Intelligence (XAI))


Abstract

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain the aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by the International Organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI) which is one of the most researched topics in computer science has not quite reached the end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and what are the limitations, then synthesise the findings into a conceptual framework, named DPP model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by the end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

Bibtex

@article{Degas 6375,
author = {Augustin Degas and Mir Riyanul Islam and Christophe Hurter and Shaibal Barua and Hamidur Rahman and Minesh Poudel and Daniele Ruscio and Mobyen Uddin Ahmed and Shahina Begum and Md Aquif Rahman and Stefano Bonelli and Giulia Cartocci and Gianluca Di Flumeri and Gianluca Borghini and Fabio Babiloni and Pietro Aric{\`o}},
title = {A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory},
month = {January},
year = {2022},
journal = {Applied Sciences - Computing and Artificial Intelligence (Special Issue: Explainable Artificial Intelligence (XAI))},
url = {http://www.es.mdh.se/publications/6375-}
}