UTM regulatory concerns with machine learning and artificial intelligence

Date

2022-10-31

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Department

Type

Conference paper

ISSN

2155-7195

Format

Free to read from

Citation

Ryan R, Al-Rubaye S, Braithwaite G. (2022) UTM regulatory concerns with machine learning and artificial intelligence. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, 18-22 September 2022, Virginia, USA

Abstract

Artificial intelligence (AI) and machine learning will have a significant impact on the application of drones and the integration of universal/unmanned traffic management (UTM) that relate to unmanned operations in urban environments at very low-level airspace. Artificial intelligence will necessitate high levels of automation and act as an enabler with respect to the integration of unmanned and manned aviation and will ultimately enable safe operations with respect to high numbers of drones utilising the same airspace, and more specifically with respect to detect and avoid capability. AI is going to be heavily developed and utilised by organisations that certify as U-space service providers (USSP’s) when providing a service to Unmanned Aerial Systems (UAS) Operators. The equipment utilised by UAS Operators will to some extent already benefit from AI, but the level of automation is currently constrained by regulation. A legal framework must exist, as AI will not only have a significant impact upon existing laws but will ensure a framework that facilitates safety and the fundamental rights of citizens and businesses with respect to AI. The EU has published a proposed law, namely the Artificial Intelligence Act as permitted under Article 114 of the Treaty on the Functioning of the European Union (TFEU).

Description

Software Description

Software Language

Github

Keywords

Artificial intelligence, U-space service providers (USSP’s), Universal Traffic Management (UTM), Unmanned Aerial Systems (UAS), automation, regulation, legal framework, human machine teams, human in the loop, human on the loop, human outside the loop, EU AI Act, machine learning (ML)

DOI

Rights

Attribution-NonCommercial 4.0 International

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