Each vessel has its own way of sailing in the port region. Any autonomous vessel navigating such a scene should be able to predict the trajectories of surrounding ships and adjust its behaviour to avoid a collision. In this paper, combined with the sequence prediction method, a Long Short-Term Memory (LSTM) model is proposed to predict the trajectories of the vessels. The ground-truth Automatic Identification System (AIS) data in the port of Tianjin, China are used to train and test the proposed model. The experimental results prove that our model can predict ship trajectories accurately, and it is applicable to the autonomous navigation system.

Liquid sloshing in ship tanks would have a direct effect on ship dynamic stability, and thus is of great importance for navigation safety. To calculate the sloshing-induced force on real ship tanks, this paper presents an approach to numerically simulate the liquid-sloshing in complex tanks using the moving particle semi-implicit (MPS) method. The sloshing-induced force is numerically calculated and used to investigate the effect of different excitation conditions in which the realistic ship motions under different loading conditions have been taken into account. Simulation results show that the maximum sloshing-induced force is much bigger than the corresponding static one. Meanwhile, both the rolling angle and period have significant effects on liquid sloshing.

This paper investigates the flow field around the KRISO Very Large Crude Carrier (KVLCC2) model at different drift angle using the RANS method in combination with the Re-Normalization Group (RNG) $k-\epsilon$ and Shear Stress Transport (SST) $k-\omega$ turbulence models. The hydrodynamic forces and yaw moment acting on the KVLCC2 model are calculated under the different turbulence models and drift angles, and the numerical results are compared with previous experimental results. The accuracy of the numerical results is found to be acceptable, especially under the SST $k-\omega$ turbulence model. Three different mesh conditions are employed to perform the verification and validation based on the methodology and procedures suggested by ITTC. The surface pressure distribution, vorticity distribution, and surface flow features around the hull are observed and analysed. The maneuvering derivatives are calculated based on numerical results to create the mathematic model of ship maneuvering motions in the marine simulator and predict the ship maneuvering motions.

In this paper, we focus on the safety supervision of inland vessels. This paper especially aims at studying the vessel target detection and dynamic tracking algorithm based on computer vision and the target fusion algorithm based on multisensor. For the vessel video target detection and tracking, this paper analyzes the current widely used methods and theories. Additionally, facing the application scenarios and characteristics of inland vessels, a comprehensive vessel video target detection algorithm is proposed in this paper. It is combined with a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling. Besides, for the multisensor target fusion, the processing method of laser point cloud data and automatic identification system (AIS) data is analyzed in this paper. Based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction. Finally, through this method, a set of vessel situation active intelligent perception systems based on multisensor fusion was developed. Experiments show that this method has better environmental applicability and detection accuracy than traditional manual detection and any single monitoring method.

Ocean Wave Rendering with Whitecap in the Visual System of a Maritime Simulator

Autonomous berthing of unmanned surface vehicles (USVs) requires high-precision positioning and accurate detection of navigable region in complex port environments. This paper presents an integrated LiDAR-based approach to address these challenges. A high-precision 3D point cloud map of the berth is first constructed by fusing LiDAR data with real-time kinematic (RTK) measurements. USV pose is then estimated by matching real-time LiDAR scans to the prior map, achieving robust, RTK-independent localization. For safe navigation, a novel navigable region detection algorithm is proposed, which combines point cloud projection, inner-boundary extraction, and target clustering. This method accurately identifies quay walls and obstacles, generating reliable navigable areas and ensuring collision-free berthing. Field experiments conducted in Ling Shui Port, Dalian, China, validate the proposed approach. Results show that the map-based positioning reduces absolute trajectory error (ATE) by 55.29% and relative trajectory error (RTE) by 38.71% compared to scan matching, while the navigable region detection algorithm provides precise and stable navigable regions. These outcomes demonstrate the effectiveness and practical applicability of the proposed method for autonomous USV berthing.