# A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS
## 1 Introduction  
Nowadays, the management of maritime safety, and pollution prevention of the marine environment is the central task of the international maritime industry [1; 2; 3]. Each collision caused by the manipulation of the crew not only causes material damage and threats to human life, but is also a great threat to the marine environment [4]. Thus, the International Regulation for preventing collisions at Sea (COLREGS) has been fully adhered to, combined with marine support systems to minimize crashes, such as the recommended use of automatic identification system (AIS) equipment and automatic radar plotting aids (ARPA) in practice [5; 6]. However, COLREGS-72 regulations have few quantitative features, do not offer accurate or tailor-made maneuvers, are highly dependent on the sense and skills of the sailors, and they are even difficult to project or guess the vessels' cruise [7]. Therefore, in recent years, there have been a number of studies using AIS information to tackle maritime collisions that have achieved a number of accomplishments. [PERSON] and et al. built a common risk model and collision warning system [8]. [PERSON] et al. analyzed the use of AIS in automatic collision, incorporated ARPA, and an electronic chart display and information system (ECDIS) [9]. [PERSON] and [PERSON] designed a collision detection system by collecting global positioning system (GPS) and AIS information, and then issued alerts for fishing vessels via the Network Operations Center (NOC) [10]. Collision assessment is the basis of a crash detection and prevention system. It is very important to avoid collisions through the timely detection and immediate warning of the collision risk, thus ensuring maritime safety and avoiding casualties. This assessment is based on the development of a collision risk index (CRI) [11; 12]. The value of the CRI is influenced by a variety of factors. The time to closest point of approach (TCPA), the distance at the closest point of approach (DCPA), the distance from the target vessel, and the relative bearing are the most significant influence factors. AIS devices on board use weighting methods, including DCPA and TCPA. This is the simplest method, but theresults are not very accurate and not very high in some special situations. Furthermore, there are some methods for calculating the CRI through neural networks and fuzzy theory, which have the highest accuracy [11; 13]. However, it is difficult to establish functions and to determine the initial conditions, which leads to limitations to the application of these methods in practice [14; 15; 16]. Different algorithms have been developed and used for navigation, guidance, and control (NGC) such as avoiding obstacles, and each method has its advantages and disadvantages [17; 18; 19]. In general, the issues related to avoiding collisions, warnings, motion control, and navigation guidance are divided into three basic groups: (a) Target point stabilization, which is setting the position of the ship in target ship to determine the collision capability, collision time, and collision location of the event [20; 21; 22]; (b) Collision avoidance processing, which implements the warning and proposed plan of collision avoidance by the combination of systems on board including the AIS, ARPA, ECDIS, very high frequency (VHF) equipment, and the warning signal [23; 24]; and (c) Trajectory tracking, which includes navigation tracking the required ship moves in the reference curve according to route planning, and supporting the management of the volume of vessel traffic in the port [25; 26]. However, as [PERSON] et al. reported, many maritime accident investigations have shown that 75-96% of marine accidents are caused by human failure and collisions at sea involve the inaccuracy of the COLREGS rules in their implementation [27]. While engaging a seasoned crew will reduce the rate of accidents, crew costs currently account for approximately 35-68% of the daily operating costs [28].