A framework for quantification of coupling risk in the transition between operational modes of MASS

The rapid development of Maritime Autonomous Surface Ships (MASS) marks a significant leap towards enhanced efficiency and safety in the maritime industry. In this context, a transition process between modes of operation (MoO) is crucial for maintaining navigational safety and operational effectiveness. These transitions, however, may introduce complex functional couplings that generate performance fluctuations, posing new challenges for navigation safety. Despite increasing focus on MASS safety, current research lacks a systematic approach to identify coupling risks during MoO transitions. To fill this gap, this study presents a four-step quantitative framework that integrates Sequentially Timed Events Plotting, the Functional Resonance Analysis Method and Monte Carlo simulations. The framework identifies key functions and their interrelations during MoO transitions, quantifies the performance variability in the couplings, and pinpoints high-risk couplings and resonance paths. A case study focusing on a MASS encountering fishing vessels without Automatic Identification System data is presented to illustrate the framework’s application. The results reveal high-risk resonance paths involving situational awareness of the autonomous navigation system, ship-to-shore data transmission, and decision-making by seafarers on board and remote operators. Twelve measures are proposed, including AI-driven monitoring, multi-sensor fusion, and adaptive communication protocols to mitigate operational risks to ensure safe MoO transitions.