A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

Abstract

Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

“…The DNSD was formed by fully considering the expansion effect of various factors on the BNSD. In the relative researches, scholars generally use the method of fuzzy mathematics and membership function [22,33,[43][44][45] to study the influence of various factors on navigation safety. In this paper, the influence of each factor on the NSD was given by an extension function.…”

“…Most of these models are built by statistical methods, analytical methods and artificial intelligence methods [23]. Collision avoidance [27,28], marine traffic simulation [29,30], navigation risk assessment [26,[31][32][33] and optimal path planning [34,35] have been studied by applying these models.…”

Inference Rule of Collision Risk Index based on Ship Near-Collision via Adaptive Neuro Fuzzy Inference System

“…Therefore, near-collision, which was a situation in which there was the danger of collision between ships approaching each other, but with no collision eventually occurring, either due to deceleration, or evasion by the change of course, was used. In order to detect a number of near-collision, ship domain was utilized for decision of near-collision as criteria not overlapped between ship domains [21,22]. Thus, in this study, near-collision was decided according to proposed methods using ship domain.…”

COLREGs-Compliant Unmanned Surface Vehicles Collision Avoidance Based on Multi-Objective Genetic Algorithm

“…Intelligent expert system based approaches were applied to research ship avoidance collision in [16,17]. Neural networkbased ships collision avoidance problems were studied in [18,19].…”

“…However, as pointed out in [22], intelligent expert system, neural networks and fuzzy logic-based ship collision avoidance algorithms have their own merits and shortcomings. For example, intelligent expert system approaches have a high professional level and excellent reliability but they are difficult to make creative answers to unexpected situations [16], while during a multi-vessels encounter situation, sometimes ships will involve in an emergency situation. Neural network methods can identify the nonlinear and complex ship motion model, but their effects are highly dependent on training evolutionary learning data [16].…”