Assessment Method Based on AIS Data Combining the Velocity Obstacle Method and Pareto Selection for the Collision Risk of Inland Ships

Abstract

A ship collision risk assessment model is an essential part of ship safety navigation. At present, the open water collision risk assessment model (such as the closest point of approach) is applied, but a ship collision risk model suitable for inland rivers is still in the exploration stage. Compared with open waters, the inland waterway has a larger density of ships, and the land and water environments are complex. The existing risk assessment models lack adaptability under the conditions of inland navigation. Therefore, this paper proposes a real-time collision risk assessment method for ships navigating inland rivers. This method utilizes the information of ships’ size in the automatic identification system (AIS) to construct the velocity obstacle cone between convex polygonal targets using the velocity obstacle method. Then, according to the geometric relationship between the relative velocity of two targets and the velocity obstacle cone, a new collision risk assessment model is defined. This model defines two indicators to evaluate the navigation collision risk: the degree of velocity obstacle intrusion (DVOI) and time of velocity obstacle intrusion (TVOI). These two indicators assess the risk of collision, respectively, from two aspects speed and course. In addition, a method using a trajectory compression algorithm to screen collision avoidance operation points in ship AIS trajectory is proposed to screen collision avoidance scenarios in the Yangtze River waterway. The effectiveness of the proposed collision risk model is verified in course-keeping and collision avoidance scenarios and compared with the traditional closest point of approach (CPA) method. The results indicate that the evaluation model for collision risk assessment is more accurate than the CPA method in all scenarios. Finally, this paper uses the Pareto selection algorithm to combine DVOI and TVOI, which can identify the ship that poses the greatest risk to our ship.