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Ship collisions are a major maritime accident; various systems have been proposed to prevent them. Through investigating and analyzing the causes of maritime accidents, it has been established that ship collisions can either caused by delaying actions or not taking the sufficient actions to avoid them. Recognizing the limitations in providing quantitative numerical values for avoiding ship collisions, this study aimed to use Bayesian regularized artificial neural networks (BRANNs) to suggest the proper time and sufficient actions required for ship collision avoidance consistent with the Convention on the International Regulations for Preventing Collisions at Sea. We prepared the data by calculating the proper times and sufficient actions based on precedent research and used them to train, validate, and assess the BRANNs. Subsequently, an artificial neural network controller was designed and proposed. The data of the proposed neural network controller were verified via simulation, validating the controller. This study is limited in cases such as overtaking a ship in front. However, it is expected that this controller can be improved by establishing the criteria for an appropriate overtaking distance after further examining the closest point of approach (CPA) and time to the CPA (TCPA) for overtaking a ship in front and using the method presented herein.

Maritime education and training (MET) for seafarers who operate ships has struggled to flexibly adapt to technological and environmental changes. In particular, as social demand for online MET arose due to COVID-19, the need for sustainable MET beyond traditional teaching methods grew exponentially. In order to identify the most optimal MET methods among face-to-face and online methods, this study reviewed the concepts and applications of existing MET methods, grouped them using a fuzzy analytic hierarchy process, and supplemented this structure through a designed survey. The results showed that the online methods had the greatest weight, and the “XR (extended reality) within the metaverse” teaching method had the highest priority. This study identified which MET methods should be prepared for the post-COVID era through quantitative analysis. We confirmed the need for attention to XR within the metaverse as a field of online methods in the future. Furthermore, our findings reveal that online education platforms via metaverse-based “expansion” and “connection” are needed, and pave the way for future research to expand empirical studies on MET satisfaction regarding existing International Maritime Organization model courses.