This work presents a data-driven approach for the automated risk estimation of the voyage of a vessel or ship. While the industry is moving from a compliance-based framework with existing rules to a risk-based one, there is also a need to monitor the risk of a vessel from the perspective of the navigation. This is of even higher importance for the case of autonomous ships. Built based on the state-of-the-art mathematical representation, the navigation feature, each existing voyage is transformed into a corresponding series of points in d-dimensional space. During the stage of pre-processing, given a set of historical Automatic Identification System (AIS) data, those records that belong to the same vessel within a certain period of time are taken as a voyage and mapped to the corresponding space of the navigation feature. After the pre-processing and during the online monitoring, the current trajectory of the vessel is transformed into the corresponding representation in the same way. Based on a nearest-neighbor search scheme, the distance from the nearest neighbor is taken as the risk of the current voyage. In other words, the deviation from the closest route in the historical data is taken as the risk. The developed method has demonstrated encouraging performance on a set of challenging historical AIS data from the Australian Maritime Safety Authority, covering three regions in the Australian territory, namely, the Bass Strait, the Great Australian Bight and the North West.

中文翻译：

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基于导航特征学习的自主船舶航行风险自动估计

这项工作提出了一种数据驱动的方法，用于船舶或船舶航行的自动风险评估。虽然该行业正在从现有规则的基于合规的框架转变为基于风险的框架，但也需要从航行的角度监控船舶的风险。这对于自主船舶的情况更为重要。基于最先进的数学表示，导航功能，每个现有的航程都被转换为相应的一系列点d维空间。在预处理阶段，给定一组历史自动识别系统（AIS）数据，将一定时间内属于同一艘船的那些记录作为航次，映射到导航特征的相应空间. 在预处理之后和在线监测过程中，船舶的当前轨迹以同样的方式转化为相应的表示。基于最近邻搜索方案，将与最近邻的距离作为当前航次的风险。换言之，将与历史数据中最近路线的偏差视为风险。所开发的方法在澳大利亚海事安全局的一组具有挑战性的历史 AIS 数据上表现出令人鼓舞的性能，