An Ontology to Classify Marine Encounters According to COLREG

In recent years, there has been a rapid increase in interest in computer-governed vessels, commonly referred to as Maritime Autonomous Surface Ships (MASSs), within the global maritime cluster. As MASSs are expected to enhance operational efficiency at sea and reduce the need for human operators in hazardous working environments, both industry and academia are investing an increasing amount of resources into this emerging technology. The first prototypes of computer-governed vessels are already operating at sea. The development and construction of MASSs present challenges, with navigation safety being a key concern. As they operate alongside crewed vessels, ensuring they function safely and pose no threat to seafarers, yachtsmen, or passengers is essential. The best practices for the safe operation of vessels are defined by the Convention on the International Regulations for Preventing Collisions at Sea (colreg), making it an ideal template for a MASS operating system. However, colreg rules are designed for human interpretation and cannot be directly processed by a computer agent, with some containing ambiguous provisions. To address these issues, we present an ontology for the key colreg rules based on an top-level ontology (dolce), to enhance semantic clarity and interoperability. The aim is twofold: to provide a common framework for interpreting navigation data in relation to colreg, and to establish a knowledge base that classifies marine encounter scenarios. As a result, we show that ontological reasoning can accurately classify maritime encounters and suggest the required behaviour of vessels according to colreg.