Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship interaction features essential in collision scenarios. This study proposes a decision-making framework for collision avoidance, particularly focused on the interaction between autonomous and conventional vessels. The framework integrates a human-preference-aware navigational trajectory prediction model into the collision avoidance process, enhancing the decision-making capabilities in dynamic and interactive environments. We first model human-controlled ship navigational preferences using a Long Short-Term Memory (LSTM) autoencoder combined with K-means clustering, by extracting key preferences from ship pairs identified through AIS data. These preferences, which reflect strategic trajectory adjustments in response to collision risks, are then incorporated into trajectory prediction using a Multi-Task Learning Sequence-to-Sequence (seq2seq) attention LSTM model. The predicted trajectories provide a basis for the decision-making framework, including a local path planner and a trajectory tracking controller, designed to dynamically adjust the predicted reference path for collision-free navigation and ensure its accurate tracking. The framework was validated using AIS data from the port of Rotterdam, identifying four distinct navigational preferences by combining an LSTM-autoencoder and clustering techniques and demonstrating improved prediction accuracy compared to other existing models. Simulation tests demonstrate that the framework utilises the predicted trajectories to inform decision-making, ensuring accurate path tracking while dynamically addressing collision risks for autonomous ships. By providing preference-aware and adaptive reference trajectories, the framework reduces the likelihood of MASS trajectory misinterpretation by conventional ships, thereby supporting proactive collision avoidance in mixed waterborne transport environments.

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This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

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Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and more efficient mode of transportation and a potential solution to reduce the workload of seafarers. Despite the highly sophisticated autonomous systems that enable MASS to make independent decisions, the presence of humans on board or in the loop of safety management highlights the need for effective human-machine interaction. However, a potentially systematic review of critical aspects of human-MASS interaction has not yet been conducted. In this paper, we aim to fill this gap by reviewing the literature related to human-MASS interaction from four crucial perspectives: the state of the art of human-MASS interaction, situational awareness for MASS, collision avoidance methods for MASS within a mixed waterborne transport system (MWTS), and human trust in MASS. Our review reveals that human-MASS interaction for safety and efficiency mainly focuses on four key aspects: (i) human factors, (ii) available technologies supporting the autonomy of MASS, (iii) system analysis and design for human-MASS interaction, and (iv) potential requirements regarding regulations. Moreover, we provide a detailed discussion of the three fundamental factors that influence human-MASS interaction, including situational awareness, decision-making for MASS in a mixed waterborne transport system, and human trust in the autonomous system of MASS. Finally, based on our analysis, we propose an integrated framework of human-MASS interaction in which these human factors are taken into account. We anticipate that these factors and their interaction will receive more attention to improve the safety and efficiency of MASS.

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The development of advanced ship positioning and intelligent sensing technologies has transformed navigation at sea, moving beyond reliance on captains’ experience and standard routes. The trajectories traversed by ships at sea contain valuable data that can be mined to map maritime transportation networks and inform intelligent navigation systems. Ship trajectory data at scale enables discovery of the underlying network of maritime routes, providing key insights for applications like intelligent navigation, abnormal behavior detection, trajectory prediction, and maritime traffic pattern analysis. This study reviews the development of research on maritime route networks (MRNs) derived from ship trajectory data. It summarizes the technical process to construct a MRN, contrasting approaches for identifying waypoints, extracting routes, and representing the overall maritime traffic network structure. Finally, this study explores potential applications of MRNs and anticipates promising future research directions in this domain.

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The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

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The safety of maritime autonomous surface ships (MASS) in mixed waterborne transport system (MWTS) depends on effective situational awareness (SA) distribution among MASS, manned ships, and various stakeholders, such as Vessel Traffic Service (VTS), Remote Control Center (RCC) and Fairway Shipping Agency. This paper focuses on the research question: How can situational awareness be effectively distributed among these entities in mixed waterborne transport? The research objective is to develop a distributed situational awareness framework that unifies SA among these stakeholders, ensuring safe navigation and compatibility with users of different roles. To achieve this objective, the proposed framework incorporates three key concepts: individual SA, authority-based SA, and distributed SA. Individual SA, previously introduced in our study, is responsible for each ship's SA, while authority-based SA accounts for the SA of human operators supervising the waterborne transport system, such as VTS operators and fairway agency personnel. Distributed SA generates guiding messages for ships based on the situational awareness from both individual SA and authority-based SA, thereby enabling regulation-based and traffic control-based recommendations for waterborne transport (e.g., ship speed and course adjustments). The research methodology employs ontology-based modelling to implement the framework, constructing a domain knowledge network. A case study is conducted as an essential part of the research methodology, presenting how the framework perform the situational awareness from different aspects and inconsistency detection among manned ships, MASS, VTS operators, and so on. Semantic Web Rule Language (SWRL) is utilized to detect inconsistencies and generate guidance messages for ships. Through these cases, we demonstrate how the proposed Ontology-based framework can reconcile inconsistencies between individual and authority-based SA, leading to a safer and more effective waterborne transport.@en

Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities.@en

Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities.@en

With the continual development of modern transportation technology and artificial intelligence technology, how to recognize the complex phenomenon of ship behavior existing in maritime traffic has become a hot topic. Maritime traffic is a complex system, the emergence of ship behavior is a leading cause of traffic complexity, and make up the core ideas of this research. This research studies ship behavior from three aspects: ship individual behavior, ship-ship interaction and multi-ship behavior. According to the movement state attribute, the improved Social Force Model has been developed by considering of the interactive effects between ships. On that foundation, the complex network model has been built to analyze the emergence of multi-ship behavior in a macroscopic view. Through experimental analysis of ship behavior in different scenarios, the results show that the repulsive force between ships changes in the ship behavior dynamic model can express the dynamic characteristics of the ship. And structural entropy in marine traffic situation complex network has been proved to describe the maritime traffic system. As such, the framework proposed in this paper can provide a new perspective for further understanding and research of ship behavior.

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Many projects related to maritime autonomous surface ships (MASS) have been proceeding to date, which promotes the commercialization of MASS. It is anticipated that there will be ships with different degrees of autonomy coexisting in a waterborne transport system (WTS) in the near future, forming a mixed waterborne transport system (MWTS). To ensure navigational safety, the ship needs to be well aware of the situation in real time, i.e. be able to exhibit situation awareness. The inconsistency of comprehension within SA of the same situation could occur in the MWTS, leading to a potentially dangerous situation. As such, it is essential to unify the SA framework for MASS to eliminate the inconsistency with human operators. It is challenging but necessary for a MASS to accomplish the process of situation awareness involving perception, comprehension, and projection. Especially the part of comprehension is the core element that needs to be addressed well and enhanced further. One possible way to reach it is to integrate the information given by the perception layer, projection layer, as well as additional domain knowledge like navigational rules to conduct further analysis. Accordingly, the current paper proposes a method for knowledge integration of SA for the MASS. The method realizes four capabilities of SA to satisfy relevant requirements in maritime domain: a general map, risk assessment enrichment, temporal and dynamic features, as well as a supplement of domain knowledge. For that purpose, the paper takes two steps: (i) constructing a SA framework for MASS, in which the entities related to SA are classified to different categories. (ii) proposing an ontology-based SA comprehension model where the information of entities are integrated together and then the SA can be depicted for MASS in real time. A case study is provided to how the model can be applied in maritime domain, in which a MASS is approaching a port executing its tasks. As a result, the proposed method can relate the information provided by both the perception and projection layers, and domain knowledge in the form of a knowledge graph to depict the real-time situation. The results show that the method is feasible to provide potentials to the MASS to be aware of the situation in real time considering domain knowledge. The method can be applied to MASS for the information fusion of situation awareness, which also can be supplied to Maritime Safety and Security (MSS) organizations for traffic surveillance of WTS.@en

Safety analysis according to the spatial distribution characteristics of maritime traffic accidents is critical to maritime traffic safety management. An accident analysis framework based on the geographic information system (GIS) is proposed to characterize the spatial distribution of maritime traffic accidents occurring in the Fujian sea area in 2007–2020 by employing kernel density estimation and spatial autocorrelation techniques. The sea area is divided into various grids, and in each grid, the mapping relationships between the number and severity of the traffic accidents and the traffic characteristics are established. Machine learning (ML) technology is used to assess whether a grid area is an accident-prone area and to predict accident severity in each grid. The accident prediction of different ML models, including random forest (RF) model, Adaboost model, gradient boosting decision tree (GBDT) model, and Stacking combined model, were compared. The optimality of the Stacking combined model was verified by comparing the experimental results of this model with those of classical prediction models, convolutional neural network (CNN), long short term memory (LSTM), and support vector machine (SVM). According to the results, the maritime accident data set of the entire Fujian sea area shows typical clustering characteristics and positive spatial correlation. That is, the kernel density estimation indicates that subareas, including the Ningde sea area, Fuzhou sea area, and Xiamen sea area, generally have high densities of maritime accidents and the highest risk value within the whole Fujian sea area. High-high accident clustering, that is high cluster areas neighbored by other areas of high cluster, is mainly seen in the Ningde and Fuzhou sea areas, while the Xiamen, Putian, and Zhangzhou subareas show low-low clustering, which are low clusters neighbored by low clusters. Among the ML models, the Stacking combined model shows high accuracy, precision, recall, and F1-score values of 0.912, 0.910, 0.912, and 0.904 in predicting whether a grid area is an accident-prone area and 0.750, 0.745, 0.750, and 0.746 in predicting the accident severity in the grid, indicating its superior maritime traffic accident prediction performance. Based on our analysis of the distribution characteristics and geospatial data, our proposed method demonstrates effective and reliable risk prediction.@en

Aiming at the cognition problem of systematic ship behavior in harbor, a behavior recognition model based on semantic reasoning based on discrete event system modeling theory was proposed. Firstly, the hierarchical modeling of the behavior of ships in port waters is divided into data layer, event layer, activity layer and process layer. Based on a certain theoretical understanding of ship behavior on different time scales and space scales, build a ship behavior cognitive model; Secondly, in the data layer, the trajectory key point detection and segmentation extraction of the motion trajectory of port ship AIS data are performed by integrating port navigation rules to realize the labeling of ship trajectories. Finally, based on the labeling results of the data layer, the ontology is used to make inferences to discover the implicit ship behavior, and realize the trajectory of the ship from the data layer to the semantic layer. Experiments were performed using Xiamen Port data. The experimental results show that the behavioral cognitive ontology based on discrete system modeling can realize the cognitive and semantic reasoning of ship behavior at different time and space scales.@en