Certification and Trustworthy AI
Authors: Önder Hamamcıoğlu (Carinthia University of Applied Sciences) , Semih Bajrami (Carinthia University of Applied Sciences) , Viktor Komyshan (Carinthia University of Applied Sciences) , Gehan Dasanayake (Carinthia University of Applied Sciences) , Mathias Brandstötter (FH Kärnten Gemeinnützige Gesellschaft mbH)
Maritime Autonomous Surface Ships (MASS) challenge safety frameworks originally developed for conventionally crewed vessels. Although autonomous navigation algorithms have advanced significantly, a critical gap remains in the hardware and control architectures required to deploy them safely in real maritime environments. This paper examines the legal and operational constraints affecting MASS under current international maritime frameworks and reviews the associated challenges of multi-sensor perception and remote human--machine interaction. To address these issues, the study applies System-Theoretic Process Analysis (STPA) to identify unsafe control actions and derive safety constraints at the organizational and supervisory control levels. Based on these results, the paper proposes a safety-driven hardware and control architecture for automated surface vessel systems. The architecture is intended to function as an assurance layer around AI-enabled autonomy by combining hardware redundancy, real-time diagnostic monitoring, independent safety controllers, and mechanisms for safe supervisory intervention. In doing so, it provides the structural basis for fault-tolerant operation, controlled degradation, and transition to a minimum-risk condition under abnormal or degraded conditions.
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How to Cite: Hamamcıoğlu, Ö. , Bajrami, S. , Komyshan, V. , Dasanayake, G. & Brandstötter, M. (2026) “Safety Driven Hardware and Control Architecture for Automated Surface Vessel Systems”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).