Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.@en

To improve the ability of ship behavior detection in maritime safety surveillance, a new ship behavior of frequent turning within a certain spatial range, called ship loitering, was found under the ship behavior framework of "anchored-off, straight-sailing, turning". The concept of trajectory redundancy is defined as the quantitative representation of trajectory characteristics of ship loitering. A multi-scale sliding window-based ship loitering detection method is designed to process ship trajectories with different spatial ranges and time duration. Further, a shape recognition model based on a convolutional neural network is constructed and trained to identify four typical shapes of loitering trajectories. To verify the effectiveness of the proposed method, experiments were conducted using the AIS trajectory data collected from the sea off the Strait of Juan de Fuca in the eastern North Pacific Ocean in 2017 and validated against the manual labeling results. The results illustrate that six general categories of ships produce loitering behaviors with different intentions. The characteristics of the spatial-temporal distribution of ship loitering behaviors can be represented as different shapes. The average precision of ship loitering detection reaches 96.5% and the average accuracy of four typical loitering trajectory shapes reaches 86.2%.@en