# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 1 Introduction
The maximum relative velocity, condition of the sea, and evacuation time are considered as three language input variables for the application of fuzzy logic control, and after displaying safeguarding measures on an MGIS platform, the risk index of the two ships is evaluated based on the difference in the calculated area. [PERSON] et al. [18] integrated AIS receivers and servers to collect all global positioning system (GPS) locations and AIS information of smartphones to develop a safe navigation support system to sound an alarm when the fishing vessel is approaching within 500 m of another ship in accordance with the guidelines of the Ministry of Land, Infrastructure, and Transport of Japan. [PERSON] et al. [2] developed a collision avoidance system that can alert the fishing vessels about other approaching vessels or objects. The system consists of an Arduino Uno, ultrasonic and vibration sensor, GPS, and global system for mobile communication modules. An ultrasonic sensor is used to identify the longitude and latitude of the ship or object and sends an alert to the fishing vessels in the form of a message, where a buzzer was used as an alarm. [PERSON] et al. [19] proposed an algorithm to resolve the risk associated with collision by analyzing fishing vessel activity data. The distance to the closest point of approach (DCPA) and time to the closest point of approach (TCPA) were used as variables of basic collision risk, and the fuzzy C-means clustering method was applied to analyze the fishing vessel vulnerability. These two methods were combined to reduce the navigational collision risk. As described, many studies have been conducted to prevent collisions between fishing vessels and other small ships; however, the criteria for collision risk assessment are mostly based on distance, DCPA, and TCPA. Additionally, fishing vessels differ from merchant ships in terms of their navigation environment, operational characteristics, and operating performance. Hence, the collision risk assessment standards of merchant ships cannot be directly applied to fishing vessels. Therefore, risk assessment standards that reflect the characteristics of fishing vessels and small ships are necessary [20; 21; 22]. In a previous study, a collision avoidance algorithm for fishing vessels was developed using millimeter-wave communication with a 0.1 s cycle [23]. However, the risk involved in fishing vessels should be predicted in advance because it is difficult to predict the risk due to the steering characteristics of fishing vessels, such as 50\({}^{\circ}\)-100\({}^{\circ}\) off course standard deviation in the case of fishing operations and 5\({}^{\circ}\)-10\({}^{\circ}\) in the case of sailing [24]. Additionally, frequent alarms can distract operators by preventing them from performing other operational tasks and can result in neglectful alarm deactivation [25]. Therefore, highly effective collision avoidance algorithms should generate alarms continuously, and timely warnings should be displayed only in the most dangerous situations to avoid unwarranted distraction. In this study, small fishing vessels under navigation are targeted, but vessels engaged in fishing using fishing gear that limits their maneuverability are excluded from the study. Vessels under navigation for the purpose of sailing before or after fishing are included because they are considered to be ships in the course of sailing [26]. This study applies the algorithm to a scenario-based actual ship experiment to verify its efficacy, and the alarm is analyzed by extracting actual sea-area data cases. We also compare frequent alarm occurrence zones with high-risk sea areas to determine whether the developed algorithms work effectively in hazardous areas. Figure 1 shows the flow of the study.