Collision between ships is one of the major contributors to ship accidents. To facilitate the development of the real-time collision risk analysis model for collision avoidance, in this research, an improved Rule-aware Time-varying Collision risk Model is proposed, which considers the estimation of target ship motion and the corresponding uncertainty in the risk analysis process. To make the collision risk model more in line with the actual situation, ship maneuverability, the COLREGs, and good seamanship are considered and integrated into the framework of the TCR model. Firstly, the Gaussian process is used to predict the potential trajectories of the target ship. Secondly, the Probabilistic Velocity Obstacle (PVO) model is utilized to integrate the uncertainty of the target ship's motion into the collision risk model. The collision risk is therefore formulated as a ratio of available maneuvers leading to a collision to all available maneuvers. To verify the effectiveness of collision risk, two actual target ships and six groups of collision risk detection experiments under different encounter scenarios were carried out. Compared with the traditional collision risk index model and original R-TCR model, the collision risk detected by the Improved R-TCR model is closer to the actual situation.@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

Through the continuous development of intellectualization, considering the lifecycle of ships, the future of a waterborne traffic system is bound to be a mixed scenario where intelligent ships of different autonomy levels co-exist, i.e., mixed waterborne traffic. According to the three modules of ships’ perception, decision-making, and execution, the roles of humans and machines under different autonomy levels are analyzed. This paper analyzes and summarizes the intelligent algorithms related to the three modules proposed in the last five years. Starting from the characteristics of the algorithms, the behavior characteristics of ships with different autonomous levels are analyzed. The results show that in terms of information perception, relying on the information perception techniques and risk analysis methods, the ship situation can be judged, and the collision risk is evaluated. The risk can be expressed in two forms, being graphical and numerical. The graphical images intuitively present the risk level, while the numerical results are easier to apply into the control link of ships. In the future, it could be considered to establish a risk perception system with digital and visual integration, which will be more efficient and accurate in risk identification. With respect to intelligent decision-making, currently, unmanned ships mostly use intelligent algorithms to make decisions and tend to achieve both safe and efficient collision avoidance goals in a high-complexity manner. Finally, regarding execution, the advanced power control devices could improve the ship’s maneuverability, and the motion control algorithms help to achieve the real-time control of the ship’s motion state, so as to further improve the speed and accuracy of ship motion control. With the upgrading of the autonomy level, the ship’s behavior develops in a safer, more efficient, and more environment-friendly manner.@en

Many Collision Avoidance Systems (CAS) for autonomous ships usually presume that a ship's dynamics are completely known in advance. However, precise parameters for ships in different operating conditions are, in fact, uncertain and unknown. The parameter identification of ship dynamics is challenging and time-consuming. Thus, uncertainties in the ship dynamic model are inevitable, which can lead to errors between real trajectories and predicted trajectories. These errors might result in an unexpected collision between ships. Therefore, it is necessary to consider tracking errors in the CAS, which is missing in most existing CAS. This article proposes a way to incorporate the errors in CAS. Specifically, a Velocity Obstacle (VO) algorithm is employed to find collision-free velocities with estimated tracking errors. Firstly, the ship is assumed to be a "black box" whose inputs and outputs are observable, while the internal workings are unknown. Secondly, parameters optimization of a PID controller are employed to determine the best feedback gains for tracking given trajectories; Thirdly, the maximal tracking errors for controlling the ship to arbitrary velocities are estimated. Finally, the maximal error is added to the safety distance and the VO algorithm is employed to find a collision-free solution. The proposed Unknown-Dynamics CAS (UD-CAS) can support the upgrade of existing conventional ships to Type I-III maritime autonomous surface ship.@en

Conflict detection is a vital step of collision prevention at sea, determining if there is a risk of collision and when to take preventing actions. This article proposes a practical Rule-aware Time-varying Conflict Risk (R-TCR) for ship collision avoidance. Considering maritime practice, the conflict risk measure takes the ship maneuverability, the COLREGs, and good seamanship into account in the conflict risk measure. Specifically, the conflict risk is formulated as a ratio of achievable maneuvers leading to a collision to all achievable maneuvers. Simulations are carried out to show the characteristics of R-TCR. The results show that the R-TCR evaluates the entire conflict risk incorporating COLREG rules, multiple targets, different maneuverability, and varying ship domains. Finally, the proposed measure is applied to analyze the collision accident between two ships. Compared with the conventional risk indicators, the proposed R-TCR can deliver extra information to users, such as providing early warning, showing the room-for-maneuver, and suggesting evasive actions. Besides, the extra information also supports collision avoidance for autonomous ships. @en

Conflict detection is a vital step of collision prevention at sea, determining if there is a risk of collision and when to take preventing actions. This article proposes a practical Rule-aware Time-varying Conflict Risk (R-TCR) for ship collision avoidance. Considering maritime practice, the conflict risk measure takes the ship maneuverability, the COLREGs, and good seamanship into account in the conflict risk measure. Specifically, the conflict risk is formulated as a ratio of achievable maneuvers leading to a collision to all achievable maneuvers. Simulations are carried out to show the characteristics of R-TCR. The results show that the R-TCR evaluates the entire conflict risk incorporating COLREG rules, multiple targets, different maneuverability, and varying ship domains. Finally, the proposed measure is applied to analyze the collision accident between two ships. Compared with the conventional risk indicators, the proposed R-TCR can deliver extra information to users, such as providing early warning, showing the room-for-maneuver, and suggesting evasive actions. Besides, the extra information also supports collision avoidance for autonomous ships. @en