面向海事自主船舶自动化透明度的可解释决策支持与自适应人机接口综述
Review of Explainable Decision Support and Adaptive Human-Machine Interfaces for Automation Transparency in Maritime Autonomous Surface Ships
AI总结：
本综述针对海事自主船舶自动化透明度，整合100项研究，提出自适应透明度框架，明确透明度设计策略与关键近阶段改进手段，以降低认知负荷、提升接管及时性并保障安全运行。
摘要：
海事领域的自主航行正随着人工智能、传感和连接技术的进步而加速发展，不透明的决策制定以及校准不佳的人机交互仍是安全采用的关键障碍。本文综合了100项关于海事自主船舶（Maritime Autonomous Surface Ships, MASS）自动化透明度的研究，涵盖态势感知（Situation Awareness, SA）、人为因素、接口设计和监管四个方面。我们（i）将导航-制导-控制栈映射到岸基操作模式，即远程监督（Remote Supervision Mode, RSM）和远程控制（Remote Control Mode, RCM），并确定人为不安全控制行为（Human Unsafe Control Actions, Human-UCAs）在交接和应急回路中的集中位置；（ii）总结证据表明，透明度特征（决策依据、备选方案、置信度/不确定性以及合规性指标）可提升理解并支持信任校准，不过可靠性和可预测性通常主导信任；（iii）提炼了三层透明度设计策略：传感器/态势感知的获取与融合、人机接口（Human-Machine Interface, HMI）/增强人机接口（enhanced Human-Machine Interface, eHMI）的呈现（文本/图形叠加、颜色编码、对话式及沉浸式用户界面），以及面向工程师的流程（弹性交互设计、验证和标准化）。我们整合了用于识别人为不安全控制行为的方法（STPA-Cog + IDAC）、定量信任/态势感知评估以及操作员工作量监测，并概述了包括国际海上避碰规则（COLREGs）形式化和航路交换在内的监管与基于规则的影响。我们提出了一种自适应透明度框架，将操作员状态估计与可解释决策支持相结合，以减少认知负荷并提升接管及时性。本综述强调可操作的性能指标显示（如最近会遇距离/时间（Closest Point of Approach/Time to Closest Point of Approach, CPA/TCPA）风险条、稳健性热图）、透明模型输出（规则可追溯性、置信度）以及训练流程（硬件在环（Hardware-in-the-Loop, HIL）/模型在环（Model-in-the-Loop, MIL）、仿真）是实现更安全海事自主船舶运行的近期手段。
英文摘要：
Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-based operational modes, namely remote supervision (RSM) and remote control (RCM), and identify where human unsafe control actions (Human-UCAs) concentrate in handover and emergency loops; (ii) summarize evidence that transparency features (decision rationales, alternatives, confidence/uncertainty, and rule-compliance indicators) improve understanding and support trust calibration, though reliability and predictability often dominate trust; (iii) distill design strategies for transparency at three layers: sensor/SA acquisition and fusion, HMI/eHMI presentation (textual/graphical overlays, color coding, conversational and immersive UIs), and engineer-facing processes (resilient interaction design, validation, and standardization). We integrate methods for Human-UCA identification (STPA-Cog + IDAC), quantitative trust/SA assessment, and operator workload monitoring, and outline regulatory and rule-based implications including COLREGs formalization and route exchange. We conclude with an adaptive transparency framework that couples operator state estimation with explainable decision support to reduce cognitive overload and improve takeover timeliness. The review highlights actionable figure-of-merit displays (e.g., CPA/TCPA risk bars, robustness heatmaps), transparent model outputs (rule traceability, confidence), and training pipelines (HIL/MIL, simulation) as near-term levers for safer MASS operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
