Quantitative human takeover reliability assessment in MASS transitions: Enhancing safety via integrated system...

Maritime Autonomous Surface Ships (MASS) with multiple modes of operation are increasingly adopted to improve efficiency and address crew shortages. Safe mode transitions, especially dependable human takeovers in urgent scenarios, are essential for the effective prevention of navigation accidents. However, existing human reliability analysis methods often overlook how information degradation and cognitive processes jointly affect takeover performance. To address this challenge, the information, decision, and action in crew context model (IDAC) is extended by introducing an external filtering stage that explicitly accounts for information loss or distortion before reaching the operator's cognition. Building on this, an integrated system control and cognitive perspective is proposed. The model combines System-Theoretic Process Analysis with the enhanced IDAC model, and embeds Bayesian Networks to identify causal chains of takeover failures, quantify Human Error Probability, identify critical factors. Through scenario-based reasoning, it further derives the key causal paths of takeover failure in urgent scenarios. A case study involving both remote and onboard takeover scenarios demonstrates the framework's applicability. Results indicate that the diagnosis and decision-making stage is the most critical, with fatigue, attention, available time and trust level emerging as dominant factors. Based on these findings, this study proposes a three-level pre-alert mechanism and targeted intervention strategies to enhance human reliability during MASS mode transitions. This study provides a scalable and behaviourally grounded framework that supports the development of takeover guidelines and safety standards, aiming to prevent navigation accidents caused by human takeover failures during the development of ship autonomy.