# Navigation Scenario Permutation Model for Training of
## 1 Introduction  
The coexistence of autonomous and conventional ships invokes questions about the safety of autonomous ships when operating in the same areas as conventional ships [1]. Concerns about the reliability of navigation safety and legal issues [2] have resulted the application of shore remote control systems in the degree of autonomy levels 2 and 3. Presently, the training of remote operators is recommended to be conducted in the simulation [3] due to the similar aspects between the simulation and the remote controlling.  
Remote controlling includes rapid assessment of a ship's situation and making effective decisions regardless of the ship's location [4]. Accordingly, the training of remote operators should be compact and effective for trainees to obtain navigation proficiency within a relatively short period of training time. Thus, massive situational education is required for the training of the remote operator. In addition, the simulation training has to reflect real-life situations for a better content of training [5] and effectiveness.  
The application of reality to the simulation education method has been an important matter in other industries as well. In the case of aviation training, challenges to reduce the gap between reality and simulation practice have been focused on by the developers of the training system in designing simulation for realism [6]. The practices of trends from the real world were considered to be included in the training simulation by replicating substantial aspects of real situations [7]. In this perspective, we used the trajectory data of actual ships as the real world's trend of navigation situations.  
The ship's trajectory data has been used in various researches. The subjects of the research were such as navigation risk identification or assessment [8; 9] and ship traffic analysis [10; 11; 12]. These researchers conducted the statistical analysis of the ship's trajectory data, which is similar to this research, but for a different purpose. The other research that used ship trajectory data was focused on the route generation for an autonomous ship [13]. The research took the ship's trajectory data as the historical data of mariner's experience and used various algorithms for route recommendation. The basic idea of taking historical data as a trend of actual navigation was similar to our research. However, instead of using the ship's trajectory itself, we extracted the distribution of navigation elements and applied them in the massive generation of practical navigation scenarios.  
The practical navigation scenarios that this research aimed to generate are the realistic and proper ones for the training. In the real world, the possible navigation situation would be unlimited, but not all cases are proper for the training. For example, the large multiple altering cases or circling cases can happen in the real world, but rarely. Therefore, this research analyzed the distribution of navigation elements from trajectory data and applied them to the permutation to generate massive and practical navigation scenarios to improve the simulation training of autonomous ship remote operators.