Systems driven intelligent decision support methods for ship collision and grounding prevention: Present status, possible solutions, and challenges
本文回顾了2002至2023年间船舶碰撞与搁浅预防的系统驱动决策支持方法，涵盖风险分析、损伤评估和运动预测，指出静态方法成本高且忽略实际条件，而AI与大数据驱动的快速预测是未来方向。
• Review of 20-year maritime fleet risks and accident trends. • Survey of systems driven intelligent decision support methods. • Current static methods are costly and overlook real conditions. • Emerging methods enhance collision and grounding prevention. • Future of maritime safety: AI, big data, and rapid prediction. Despite advancements in science and technology, ship collisions and groundings remain the most prevalent types of maritime accidents. Recent developments in accident prevention and mitigation methods have been bolstered by the rise of autonomous shipping, digital technologies, and Artificial Intelligence (AI). This paper provides an exhaustive review of the characteristics of fleets at risk over the past two decades, emphasizing the societal impacts of preventing collisions and groundings. It also delves into the key components of decision support systems from a ship's perspective and undertakes a systematic literature review on the foundations and applications of systems-driven decision support methods for ship collision and grounding prevention. The study covers risk analysis, damage evaluation, and ship motion prediction methods from 2002 to 2023. The conclusions indicate that modern ship science methods are increasingly valuable in ship design and maritime operations. Emerging multi-physics systems and AI-enabled predictive analytics show potential for future integration into intelligent decision support systems. The strategic research challenges include (1) underestimating the impacts of real operational conditions on ship safety, (2) the inherent limitations of static risk analysis and finite numerical methods, and (3) the need for rapid, probabilistic assessments of damage extents. The demands and trends suggest that leveraging big data analytics and rapid prediction methods, underpinned by digitalization and AI technologies, represents the most feasible way forward.