No abstract

The first ever attempt at fully autonomous dock-to-dock operation has been tested and demonstrated successfully at the end of 2018. The revolutionary shift is feared to have a negative impact on the safety of navigation and the getting of real-time situation awareness. Especially, the centralized context onboard could be changed to a distributed context. In navigation safety domain, monitoring, control, assessment of dangerous situations, support of operators of decision-making support system should be implemented in real time. In the context of autonomous ships, decision-making processes will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. At the same time, situation awareness is the key element of the decision-making processes. Although there is substantial research on situation awareness measurement techniques, they are not suitable to directly execute quantitative processing for the situation awareness of autonomous ships navigation. Hence, a novel quantitative model of situation awareness is firstly proposed based on the system safety control structure of remotely controlled vessel. The data source is greatly limited, but the main result still indicates that the probability of operator lose adequate situation awareness of the autonomous ship is significantly higher than the conventional ship. Finally, the paper provides a probabilistic theory and model for high-level abstractions of situation awareness to guide future evaluation of the navigation safety of autonomous ships.

The paper applied Principal Components Analysis Method to analyze the PSC inspection results in the area of T-MOU and P-MOU. Set up the assessment of ship detention, the ships' main deficiencies of detentions were found out by the standardization of data processing and correlation matrix calculating. Provide the basis for shipping company to master the safety management focus and pass the PSC inspection.

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

Human-related issues have become a popular topic in maritime safety research, with an increasing number of relevant research articles being published annually. However, a persistent problem in this field is that three terms, namely “human element”, “human factor”, and “human error” are used interchangeably in the literature. This issue poses questions regarding the characteristics of their usage; do these three terms have the same meaning? Herein, we conducted systematic research on the three terms by analyzing official information and published research using a collecting–classifying–summarizing policy. The results show that “human error” is easier to identify than “human element” and “human factor”, while the latter two terms have intersecting contents. These contents prompt the user to decide which term to choose depending on the situation. Herein, we aim to help scholars accurately distinguish these terms.