# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 3 Decision-Making System for MASS Operation  
Decision support technology is essential to enable the operation of ships, especially over MASS degree 3. Among various decision-making systems, decision-making technology for collision avoidance is one of the core technologies for the safe operation of MASSs. [PERSON] [24] used the collision risk model (PARK model) to display the risk of collision on the screen so that the officers could help make decisions based on that information. [PERSON] et al. [25] proposed a method to reduce ship collision accidents due to human errors by automatically suggesting solutions in color in a collision situation. Additionally, [PERSON] and [PERSON] [26] proposed an algorithm that suggests the safest athlete's defence according to the risk, taking into account the condition of the opposing ship. These technologies can be applied to MASS degrees 1 and 2, but cannot be applied to ships of MASS degree 3 and 4 that have no seafarers and will be controlled by remote operators. Therefore, a technology capable of automatically making decisions based on the collected information is required to avoid the collision of ships. [PERSON] et al. [27] proposed that a ship collision situation avoidance algorithm based on fuzzy logic, which can be developed and used in decision-making systems in autonomous vessels, and [PERSON] and [PERSON] [28] proposed the structure of a decision support system to replace experienced seafarers using a Bayesian network. [PERSON] et al. [29] suggested that the system can avoid collision through the proposed system based on the collision avoidance linear extension algorithms in various ship operation situations according to the COLREGs regulations. [PERSON] [13] proposed a decision support system to be applied to autonomous ships based on deep learning and presented possible problems and solutions to applying it to MASS operation. [PERSON] [30] proposed a trajectory base algorithm that supports decision making to avoid static and dynamic objects in complex navigation situations. [PERSON] et al. [31] proposed deep reinforcement learning to achieve intelligent obstacle avoidance of MASS in a port, which is an autonomous navigation decision-making model based on hierarchical deep reinforcement learning. [PERSON] et al. also suggested that an improved deep reinforcement learning (DRL) algorithm could effectively improve navigation safety and collision avoidance for MASS. While various decision-making technologies are being developed, research on the development of a deep learning-type decision-making system that has been verified through examples applied in vehicles will be strengthened in the future. However, the required technologies to implement and evaluate ship intelligence in transport systems are still in a preliminary stage. Therefore, a considerable amount of effort with future studies will have to be made for that area. In addition to the collision avoidance decision system, decision systems for various emergency situations based on the degree of autonomy are also required for the safe operation of MASSs. [PERSON] et al. [32] proposed a decision-making system for emergency response in the event of a ship flooding situation. [PERSON] et al. [33] developed a system that can provide the flooding level sensor data and the status of the ship's doors to assist crews in making decisions on a submerged passenger ship. This decision support system is applicable on the premise that the seafarer is on board, but since this information is essential for ships with MASS degree 3 or higher, it is necessary to continuously develop reliable technology for responding to a ship flooding situation. In addition, to support the efficient operation of ships, [PERSON] et al. [14] proposed an artificial neural network decision-making system to help optimize the use of fuel for ships, so that it can be operated efficiently, and [PERSON] et al. [34] have proposed a decision system that selects the optimal route by considering the ship's hull fatigue, sailing time, and exhaust gas reduction.