An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system

Abstract

Due to brisk industrial growth, the marine traffic has become an imperative subject in the open sea nowadays. The crew inside the vehicle traffic service (VTS) centre is facing challenging issues on account of continuous growth in vessel number. Currently, most of VTS centers’ are using the ARPA RADAR based conventional vehicle traffic management system and VTS staff has to carry out most of the things manually to guide the ship’s captain properly. Therefore, there is a strong impetus in the field of ocean engineering to develop a smart system which can take the data from RADAR and autonomously manipulate it, to calculate the degree of collision risk among all vessels from the VTS centre. Later on, the traffic management officer utilizes this information for intelligent decision making. In the past, several researchers have addressed this issue to facilities the VTS crew and captain of the ship but mostly, their research work was for academic purposes and could not get popularity because of extra manual workload. Our proposed vessel collision risk assessment system is an intelligent solution which is based on fuzzy inference system and has the ability to solve the said issues. We calculated the DCPA, TCPA, bearing and VCD among all vessels ships from the VTS centre by using conventional marine equipments and exploited the extracted information to calculate and display the degree of collision risk among all vessels. Furthermore, we developed the RADAR filtration algorithm which helps the VTS officer to gauge out the degree of collision risk around a particular ship. To authenticate the validity and to monitor the performance efficiency, we developed RADAR operated intelligent software which directly gets the required data from RADAR and displays the vessels list based on their degree of collision severity. The laboratory experiments confirm the validity of the proposed system.