Development of collision-case-based testing scenarios for validating autonomous ship collision-avoidance algorithms

The criticality of collision-avoidance technology for ensuring safe navigation of autonomous ships necessitates diverse testing scenarios that reflect complex maritime environments. However, previous testing scenarios, often based on virtual trajectories or simplified encounters, have shown limitations in adequately representing real-world conditions. This study proposes a novel framework for developing collision-avoidance testing scenarios based on actual collision cases. The framework consists of three stages: collision case collection, trajectory extraction, and scenario development. Relevant data were extracted from selected cases, and the trajectories of ships influencing the collision were combined to reconstruct the circumstances at the time of the incident. Encounter situations were then diversified by altering the roles and positions of own and target ships, and finally systematically categorised into a structured testing set. Unlike previous testing scenarios, the developed scenarios exhibit distinctive characteristics derived from actual collision cases, including situations where navigation rules cannot be strictly applied, dynamic encounters, speed variations, and environmental conditions. By reflecting real maritime environments, these scenarios provide a solid basis for validating and improving collision-avoidance algorithms. The proposed framework is expected to contribute not only to the advancement of autonomous-ship technology but also to the enhancement of maritime safety.