Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

In order to detect multi-ship encounter situations and improve the safety of navigation, this paper proposed a model which was able to mine multi-ship encounter situations from Automatic identification system (AIS) data and analyze the encounter spatial-temporal process and make collision avoidance decisions. Pairwise encounters identification results and ship motion index were combined into a ship encounter graph network which can use the complex network theory to describe the encounter spatial-temporal process. Network average degree, network average distance and network average clustering coefficient were selected. Based on the recognition results of pairwise encounter identification results, a discrete multi-ship encounter network is constructed. The process of multi-ship encounters from simple to complex to simple is mined based on the process of average network degree from 0 to 0 to obtain a continuous spatial-temporal process. The results can be used for multi-ship encounter situation awareness, multi-ship collision avoidance decision-making and channel navigation evaluation, and also provide data for machine learning. Quaternary dynamic ship domain, fuzzy logic and the weighted PageRank algorithm were used to rank the whole network risk, which is critical to “key ship collision avoidance.” This method overcame the problem that the traditional collision risk evaluation method is only applicable to the difference between two ships and ship perception. The risk rank combined with the artificial potential field method was used. Compared with the traditional artificial potential field method, this method has fewer turns and a smoother trajectory.