In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorpo-rates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.@en

The consequences caused by bridge failures owing to the ship-bridge collision are always severe in terms of loss of life, economy, and environmental consequences to individuals and societies. The previous studies focused on the ship-bridge collision mainly concentrated on passive anti-collision, such as strengthening the bridge structure or setting anti-collision facilities. Compared with the previous research, the contribution of this work is to facilitate the reduction of collision risk of ship-bridge collision from the perspective of active anti-collision. A data-driven approach for ship-bridge collision candidate detection method in inland bridge waterways is proposed in this research. The approach is mainly divided into two steps: 1) The features (channel boundary, pier domain, and ship domain) of bridge waterways are identified using Kernel Density Estimation (KDE) method based on the historical AIS data; 2) Collision candidate detection with Velocity Obstacle (VO) method considering the identified features. This work can provide beneficial support for the ship-bridge active collision avoidance system.@en

Regional risk analysis and management of maritime accidents is one of the fundamental tasks for maritime safety management. With the heavy and complicated maritime traffic in the ports and waterways, accidents, especially ship collision accidents, have been continuously posing threats to the maritime transportation system. To achieve effective and prompt identification of collision risk and to facilitate the stakeholders such as Maritime Safety Administration, this paper proposes an integrated approach for regional collision risk analysis and maritime safety management in busy ports and waterways. Firstly, regional gridding is used to link accident data and traffic data based on geographical location; Secondly, the risk model based on accident data is established. The reliability of the accident risk model is verified by data feature analysis. Finally, non-accident critical events are mined from historical accident data and traffic data as surrogate indicators of collision accidents. A regional real-time risk model is developed for integrating the accident risk model and non-accident critical events risk model by using random forest. A case study in Shenzhen port indicates that the proposed collision risk model can identify high-risk areas and facilitates maritime safety management to improve the safety level of vessel traffic in these areas. In this paper, the regional grid is used to overcome the shortcomings of different scales between data, and a real-time risk model is established by combining accidents and traffic. The 15-year maritime collision accidents are used for collision risk modeling, which improves the performance of the model.@en