Explainable AI for Safe Navigation

Explainable Artificial Intelligence (XAI) plays a key role in building trust in AI-based decision support systems and in enhancing safety in semi-autonomous ship navigation. By fusing heterogeneous sensor data, the system generates a precise and continuously updated representation of the maritime environment, which is visualized in a dynamic situational map.

This situational awareness forms the basis for the automatic detection of encounter scenarios as well as for the rule-compliant application of the International Regulations for Preventing Collisions at Sea (COLREGs). Based on this information, situation-dependent action recommendations are derived to support the crew in navigation-related decision-making.

A central added value lies in the transparent traceability of the AI recommendations: the system explains its suggestions in a comprehensible manner, highlights relevant influencing factors, and shows how decisions are derived.

By combining sensor fusion, situational awareness, and traceable decision-making processes, the system strengthens user trust. It supports well-founded decisions, promotes compliance with safety-critical regulations, and contributes to sustainably improving safety, reliability, and operational efficiency in maritime applications.