Vol 77 No 3 77312

Abstract

With the advancement of next generation information technologies, Maritime Autonomous Surface Ships (MASS) are progressively advancing. However, the dynamic uncertainties arising from multi-ship interactions in complex maritime traffic environments significantly constrain their capabilities for risk identification and adaptive switching between Mode(s) of Operation (MoO). To address this challenge, this study proposes a navigation energy field model for risk assessment that integrates multi-ship interaction features. First, maritime traffic complexity is quantified based on intrinsic ship attributes and the Potential Risk Ship Domain (PRSD) framework. Second, to address the inadequacy of conventional field theory in capturing dynamic coupling relationships between ships, a navigation energy field model is developed that incorporating multi-ship interaction characteristics, guided by quantified traffic complexity. Finally, applying the ALARP (As Low As Reasonably Practicable) principle, navigation scenarios are classified, providing a quantitative foundation to support adaptive MoO switching. The results demonstrate that the proposed method effectively reveals the risk evolutionary patterns of collective ship behaviors in multi-ship convergence and high-density traffic environments, thereby enhancing the ability of MASS to identify risks. This research provides theoretical and practical support for risk assessment and adaptive MoO management in MASS, contributing to improved navigational safety under dynamic and complex navigation situations.