Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

中文翻译：

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基于 AIS 的近碰撞数据库生成和真实避碰机动分析

经济和技术发展增加了海上交通的数量、密度和复杂性，这带来了新的挑战。一个挑战是符合船舶进入接近碰撞情况 (NCS) 所需的独特规避机动。现有规则含糊不清，并没有准确规定应该执行哪些、何时以及如何执行防撞机动 (CAM)。自动识别系统（AIS）广泛用于船舶监控和交通控制。本文提出了一种使用最接近点 (CPA) 框架处理大规模原始 AIS 数据的高效、可扩展方法。识别 NCS 以创建历史交通数据的数据库。定义、估计和分析描述 CAM 的重要特征。在高质量真实世界数据集上的应用显示了已识别情况子集的有希望的结果。未来的应用可能会在海事监管框架、导航协议合规性评估、风险评估、自动避碰以及自主船舶的算法设计和测试中发挥重要作用。