Abstract:
In the evolving landscape of North American Aerospace Defense Command (NORAD) operations, the collaboration between human operators and autonomous systems is imperative f...Show MoreMetadata
Abstract:
In the evolving landscape of North American Aerospace Defense Command (NORAD) operations, the collaboration between human operators and autonomous systems is imperative for effective Command and Control (C2) in the face of increasingly complex threats to Aerospace Defence (AD). As traditional threats persist and novel challenges such as Hypersonic Glide Vehicles emerge, the mental burden on human operators grows, necessitating the integration of Artificial Intelligence (AI) and automated tools. This paper focuses on Human Autonomy Teaming (HAT) for bridging the communication and trust gap between the human and the machine, presenting a novel cloud-compatible solution, the Verifiable Human Autonomy Teaming (VHAT) system. VHAT aims to enhance AD capabilities by automating target identification, tracking, and trajectory predictions, addressing the challenges associated with the static nature of AI decision-making. The system is designed to instill trust and allow auditing of AI decision support while featuring scalable machine learning models, advanced training capabilities, tailored AI models for AD and distributed data processing. The paper presents the conceptual system architecture of VHAT, its AI-based capabilities, and the integration of HAT capability and its features through a graphical user interface (GUI).
Published in: 2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
Date of Conference: 07-10 May 2024
Date Added to IEEE Xplore: 12 June 2024
ISBN Information: