Ship Domain Research Articles

Analysis of navigational risk indicators as a function of the ship's domain width for the selected offshore wind farm in the Baltic Sea

This study concerns the analysis of navigational risk indicators as a function of the ship's domain width estimated for nine selected representative ships sailing under various hydrometeorological conditions (average and deteriorated ones) observed within the Offshore Wind Farm to be constructed within the Polish offshore zone on the Baltic Sea. For this purpose, the authors compare three types of domain parameters according to the guidelines by the PIANC, Coldwell and Rutkowski (3D). The study enabled selection of a group of ships which can be considered safe and can optionally be allowed to navigate and/or fish in the immediate vicinity and within the offshore wind farm. The analyses required the use of hydrometeorological data, mathematical models and operating data obtained with the use of maritime navigation and manoeuvring simulators.

A Novel Ship Domain-oriented Parameter of Ship Collision Risk Considering the Ship Maneuverability and Encounter Situation

The identification of ship collision risks is an important element in maritime safety and management. The concept of the ship domain has also been studied and developed since it was proposed. Considering the existing trend that the ship domain is increasingly widely used in collision risk-related research, a new domain-oriented collision risk factor, i.e., the current state of domain (CSD), is introduced in this paper, which can effectively reflect the current state and show a certain predictability of collision risk from the perspective of the ship domain. To further prove the rationality of the CSD, a series of different simulations consisting of three typical encounter scenarios were conducted, verifying the superiority of the proposed parameter.

Ship Intrusion Collision Risk Model Based on a Dynamic Elliptical Domain

To improve navigation safety in maritime environments, a key step is to reduce the influence of human factors on the risk assessment of ship collisions by automating the decision-making process as much as possible. This paper optimizes a dynamic elliptical ship domain based on Automatic Identification System (AIS) data, combines the relative motion between ships in different encounter situations and the level of ship intrusion in the domain, and proposes a ship intrusion collision risk (SICR) model. The simulation results show that the optimized ship domain meets the visualization requirements, and the intrusion model has good collision risk perception ability, which can be used as the evaluation standard of ship collision risk: when the SICR is 0.5–0.6, the ship can establish a collaborative collision avoidance decision-making relationship with other ships, and the action ship can take effective collision avoidance action at the best time when the SICR is between 0.3 and 0.5. The SICR model can give navigators a more accurate and rapid perception of navigation risks, enabling timely maneuvering decisions, and improving navigation safety.

A real-time ship encounter collision risk detection approach in close-quarters situation

In order to identify ship collision risk for the security of maritime transportation in a close-quarters situation, a novel real-time ship collision risk awareness approach is proposed by developing a novel non-linear velocity obstacle set, QSD-NLVO. More specifically, the Quaternion Ship Domain model was introduced into the non-linear velocity obstacle algorithm, and the conflict position was reasonably defined. By replacing the conflict position with ship domain, the proposed method can more reasonably assess the safety radius of the conflict in different ship encounter scenarios. The presented model enhanced the accuracy of collision risk identification by replacing the collision position in non-linear velocity obstacle algorithm with quaternion ship domain. Finally, case studies were implemented to illustrate the effectiveness of the QSD-NLVO approach. The developed model may be utilized as a guide for investigating port traffic safety as well as a tool for maritime surveillance operators to monitor port traffic collision risks and increase traffic safety.

QSD-LSTM: Vessel trajectory prediction using long short-term memory with quaternion ship domain

Vessel trajectory prediction is a critical aspect of ensuring maritime traffic safety and avoiding collisions. The long short-term memory (LSTM) network and its extensions have represented powerful ability of vessel trajectory prediction. However, the previous studies often did not take dynamic interactions between neighboring vessels into account. Additionally, in complex traffic conditions, trajectory prediction will acquire uncertainty, and these potential negative factors can limit the prediction of future trajectory. To enhance the prediction performance, we propose an interactive vessel trajectory prediction framework (i.e., QSD-LSTM) based on LSTM, which is embedded with the quaternion ship domain (QSD). The QSD is beneficial for avoiding unwanted collision between neighboring vessels. In addition, the operation of trajectory clustering is further incorporated into our trajectory prediction framework, potentially leading to more robust prediction results. Numerous experiments have been implemented on realistic automatic identification system (AIS)-based vessel trajectories to compare our QSD-LSTM with several state-of-the-art prediction methods. The prediction results have demonstrated the superior performance of our method in terms of both quantitative and qualitative evaluations.

Maintaining Symmetry in Optimal and Safe Control of the Ship to Avoid Collisions at Sea

The aim of this study was to make a novel symmetry analysis in relation to the importance of optimizing the ship’s trajectory and safety in situations at sea where there is a risk of collision with other ships. To achieve this, the state constraints in the optimization were formulated as ship domains generated by the neural network. In addition, the use of the Bellman dynamic programming method enabled the effective optimization of the ship’s safe control. The above assumptions were confirmed by the calculations of the optimal and safe ship traffic paths for the two valid agree with COLREGs states of visibility at sea and for different densities of the dynamic programming grid. Practical conclusions from the research were formulated, and a plan for further research on methods of ensuring safety in navigation was outlined.

Risky Maritime Encounter Patterns via Clustering

The volume of maritime traffic is increasing with the growing global trade demand. The effect of volume growth is especially observed in narrow and congested waterways as an increase in the ship-ship encounters, which can have severe consequences such as collision. This study aims to analyze and validate the patterns of risky encounters and provide a framework for the visualization of model variables to explore patterns. Ship–ship interaction database is developed from the AIS messages, and interactions are analyzed via unsupervised learning algorithms to determine risky encounters using ship domain violation. K-means clustering-based novel methodology is developed to explore patterns among encounters. The methodology is applied to a long-term dataset from the Strait of Istanbul. Findings of the study support that ship length and ship speed can be used as indicators to understand the patterns in risky encounters. Furthermore, results show that site-specific risk thresholds for ship–ship encounters can be determined with additional expert judgment. The mid-clusters indicate that the ship domain violation is a grey zone, which should be treated carefully rather than a bold line. The developed approach can be integrated to narrow and congested waterways as an additional safety measure for maritime authorities to use as a decision support tool.

A Novel Method for Imputing Missing Values in Ship Static Data Based on Generative Adversarial Networks

Ship data obtained through the maritime sector will inevitably have missing values and outliers, which will adversely affect the subsequent study. Many existing methods for missing data imputation cannot meet the requirements of ship data quality, especially in cases of high missing rates. In this paper, a missing data imputation method based on generative adversarial networks (GANs) is proposed. The generative adversarial imputation network (GAIN) is improved using the Wasserstein distance and gradient penalty to handle missing values. Meanwhile, the data preprocessing process is optimized by combining knowledge from the ship domain, such as using isolation forests for anomaly detection. Statistical analysis of ship data is also conducted, including correlation analysis of ship design parameters, analysis of outliers, and analysis of missing data types. These analyses provide the basis for the proposed model. In a case study of 8167 bulk carriers, the proposed model outperformed the missing forest (MF) and polynomial fitting (PF) models, with an average error reduction of 2.4% and 6.3%, respectively. The proposed model also showed stable performance in cases of high missing rates. This study provides a new approach for estimating or imputing critical parameters of ships.

Spatial-temporal analysis method of ship traffic accidents involving data field: An evidence from risk evolution of ship collision

Collision accident is the commonest type of ship traffic accident. Analysis of the spatial-temporal characteristics during the ship encounter process is helpful to the discovery of the accident evolution mechanism. The spatial-temporal analysis method was developed for collision accidents from maritime accident investigation reports to reveal the evolution characteristics of potential collision risk (PCR) during the two ships' encounters. First, a four-dimensional interpolation algorithm under the Radar window was proposed by analyzing the uncertainty of risk collision from different ship parameters. Second, a data field cognitive model with Gaussian mixture clustering on PCR was constructed according to the Radar information of the ship encounter process. Last but not least, combining different collision avoidance scenarios under the ship domain, the spatial-temporal characteristics of PCR were discussed to reveal the evolution mechanism. Empirical evidence shows that the laws of time are equivalent to space in the four stages of the ship encounter process. Ship's parameters make a difference in risk perception under the Radar window. Different encounter situations have significant impacts on Radar-based collision avoidance.

Prediction of ship domain on coastal waters by using AIS data

Ship domain is crucial for collision risk assessments and may vary depending on the navigation area. In this study, an empirical ship domain model for coastal waters is proposed. The proposed ship domain model is planned to reveal the ship domain model for an area where no traffic separation scheme exists. In the study, dodecagon ship domain for different type and size of vessels was created. One-year (11 month) of vessel encounters were analyzed and ship domain boundaries are prepared by considering different safety criteria. The passages of the vessel from forward, aft, port, and starboard were also statistically analyzed and the changes in ship domain dimensions according to the ship type and size are revealed. This model shows the vessels behavior regardless of traffic separation rules.

Toward an Online Decision Support System to Improve Collision Risk Assessment at Sea

Increasing ship speeds and unpredictable environmental perturbations raise the difficulty of situation awareness and collision avoidance—for instance, when maneuvering in narrow and busy surroundings. Providing decision support for these operations automatically and timely is, thus, of great concern in terms of ship safety. To achieve it, we developed an online decision support system (DSS) to provide navigators with intuitive and reliable solutions, such as for collision risk and the point of collision, in real time. More specifically, the collision risk for each target ship (TS) is colored coded, and the ship domain is marked by a heat map, which can remind the navigators of the surrounding situations intuitively. Afterward, a comparative case study with and without decision support was conducted to verify the effectiveness of the DSS during the busy water navigation process around a high-fidelity simulator. Experiment results show that the collision risk can be significantly reduced with the help of the proposed DSS. It has the potential to be used onboard as a complementary service in the near future.

Pulsation Test and Analysis of Internal Gear Pump Used in Electro-hydraulic Actuator

The linear conjugate internal gear pump is widely used in the Electro-hydraulic Actuator (EHA) system because of its high efficiency and low noise, especially in the ship domain where vibration and noise requirements are very strict. To find out the reason, the calculation method of flow fluctuation and pressure pulsation at the pump outlet is obtained theoretically. First, the method of obtaining the two characteristic values of pump source flow pulsation Qs and pump source impedance Zs, and the data calculation and processing program are compiled by Matlab. Then the test platform was built, as well as the computer simulation and data collection. According to the theoretical method, the actual flow fluctuation and pressure pulsation at the pump outlet under different working conditions are measured and calculated respectively. The data of each working condition can be summarized and analyzed as: the test platform is reasonable and the data obtained from the test and calculation are reasonable; under the same frequency spectrum, the amplitude corresponding to the fundamental frequency of the motor pump is much larger than that of the pump tooth; under different working conditions, the variation trend of flow fluctuation and pressure pulsation corresponding to the fundamental frequency and the frequency doubling of the internal gear pump is obtained by testing; the specific values are smaller and suitable for the use in a silent system.

Multi-ship Encounter Situation Analysis with the Integration of Elliptical Ship Domains and Velocity Obstacles

Multi-ship Encounter Situation Analysis with the Integration of Elliptical Ship Domains and Velocity Obstacles

Collision Risk Index Calculation Based on an Improved Ship Domain Model

The traditional ship collision risk index model based on the distance at the closest point of approach (DCPA) and the time to the closest point of approach (TCPA) is insufficient for estimating ship collision risk and planning collision avoidance operations. This paper constructs an elliptical, dynamic ship domain that changes with ship speed and maneuverability parameters to overcome subjective human factors. Based on the constructed domain model, the concept of the ship domain proximity factor is introduced to improve the ship collision risk model based on DCPA and TCPA, and a risk calculation function model that considers the safety of ship navigation is constructed. The numerical calculation of the improved collision risk index calculation model confirms that the enhanced model has a higher rate of identification of risk between ships. The model is more compatible with the requirements of ship navigation decision-making and can provide theoretical support and a technical basis for research on ship collision avoidance decision-making.

A data-driven approach for ship-bridge collision candidate detection in bridge waterway

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.

Ship collision risk analysis: Modeling, visualization and prediction

Ship collisions are the primary threat to traffic safety in the sea, which can seriously threaten human lives, the environment and material assets. Therefore, the detection and analyze of ship collision risks have important theoretical significance and application value. To improve maritime safety and efficiency, we propose a modeling, visualization and prediction framework to analyze ship collision risk. In particular, to fully consider the maneuverability of the ship, we introduce the quaternion ship domain (QSD) into the vessel conflict ranking operator (VCRO). In addition, to further analyze and better understand collision risk, the kernel density estimation (KDE) model is employed to visualize the ship collision risk. The ship collision risk usually contains underlying patterns and laws. Thus, we proposed a convolutional long short-term memory network (ConvLSTM) model, which can extract spatial–temporal features and predict spatial–temporal risk. Finally, to verify the reliability and robustness of the framework, we conducted extensive experiments on the automatic identification system (AIS) data of Chengshantou water. The results show that the framework demonstrates superiority in risk calculation, visualization and prediction. Theoretically, the framework proposed in this paper can serve maritime intelligent transportation system well.

Multi-Ship Encounter Situation Identification and Analysis Based on AIS Data and Graph Complex Network Theory

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.

A Two-Stage Path Planning Algorithm Based on Rapid-Exploring Random Tree for Ships Navigating in Multi-Obstacle Water Areas Considering COLREGs

A two-stage ship path planning method is proposed, based on the Rapid-exploring Random Tree (RRT) algorithm, which is composed of global path planning and local path planning, addressing the important problem of finding an economical and safe path from start to destination for ships under dynamic environment, especially in waters with multiple obstacles and multiple target ships. The global path planning takes into consideration the ship draft and Under Keel Clearance to find navigable water using RRT, and reduces the path length and waypoints based on elliptic sampling and smoothing. In the local path planning, a dynamic collision risk detection model is constructed by introducing the Quaternion Ship Domain under a dynamic environment, and the restrictions of ship manoeuvrability and COLREGs are also involved. The simulation results show that the proposed model can find a satisfactory path within a few iterations, and keep clear of both static obstacles and dynamic ships. The research can be used to make and verify planned ship routes before sailing and to guide officers to make decisions regarding collision avoidance.

A comprehensive spatiotemporal metric for ship collision risk assessment

Collision risk assessment is an effective way to determine the potential collision and reserve time for the action taken to avoid the possible collision. This paper investigates the ship collision risk assessment problem from a new perspective that considers both the spatial and temporal factors related to the ship's navigational safety. A spatiotemporal ship domain (SD) model is developed to take both factors into account so that any traditional 2-dimensional SD model can be generalized into a 3-dimensional spatiotemporal SD model. Based on the new SD model, a comprehensive spatiotemporal metric is introduced to predict the ongoing ship collision risk, which considers not only the collision probability but also the assumed consequence. Moreover, the new assessment algorithm is continuous, and the quantitative collision risk can be obtained. Compared to the previous SD methods, the advantage of the new algorithm is that it can provide a more comprehensive and flexible risk assessment when facing different encounter situations. The effectiveness of the new risk assessment algorithm is examined by simulation studies which assesses the collision risk for a real ship collision accident and show advantages in comparison with the previous methods.

USV compliant obstacle avoidance based on dynamic two ship domains

Aiming at the problem that unmanned surface vehicle (USV) is easy to collide with each other in dynamic environment, a flexible obstacle avoidance path planning algorithm based on dynamic two ship domains is proposed. Firstly, a dynamic two ship collision risk model based on the information of encounter vessels is established, and the type of rendezvous situation is determined on the basis of the model. Secondly, according to different rendezvous situation types, the COLREGs rules are analyzed to select the avoidance action, and a virtual circular repulsion potential field is designed for the avoidance action to guide the USV to avoid obstacles flexibly. Meanwhile, the velocity elliptical repulsion potential field is constructed for emergency avoidance under the close dangerous emergency state. Finally, simulation examples of different traffic scenarios are given to verify the effectiveness and practicability of the scheme.