Search Results (65)

This study uses eye-tracking technology (ETT) to investigate discrepancies between seafarers’ perceived and actual performance during simulated maritime operations. The primary objective is to explore how misperceptions regarding the use of navigational tools—such as visual observation, radar, and ECDIS—may contribute to discrepancies in situational awareness, which is critical for safe navigation. By comparing participants’ self-reported perceptions with objective data recorded by ETT, the study highlights cognitive biases that influence navigational decision-making. Data were collected from a simulation scenario involving 32 seafarers with varying levels of maritime experience. The results reveal that participants tend to overestimate their reliance on visual observation and ECDIS, while underestimating their use of radar. These discrepancies may affect decision-making processes and could contribute to an inaccurate perception of situational awareness, although further research is needed to fully establish their direct impact on actual navigational performance. Additionally, the application of ETT identifies differences in the navigational strategies between more and less experienced seafarers, offering insights that could inform the development of training programs aimed at improving situational awareness. Statistical analyses, including Analysis of Variance (ANOVA) and Kruskal–Wallis tests, were conducted to assess the influence of demographic factors on performance. These findings suggest that ETT can be a valuable tool for identifying perceptual biases, potentially improving decision-making and enhancing training for real-world navigational tasks. Full article

Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) about the correctness of autonomy decisions, which is distorted by human anchoring and omission bias. Specifically, (i) anchoring bias (tendency to confirm prior opinion) causes the imperception of key information and miscomprehending correctness of autonomy decisions; (ii) omission bias (inaction tendency) causes the overestimation of predicted loss caused by takeover. This paper proposes a novel HAIP safety assessment method considering effects of the above biases. First, an SA-based takeover decision model (SAB-TDM) is proposed. In SAB-TDM, SA perception and comprehension affected by anchoring bias are quantified with the Adaptive Control of Thought-Rational (ACT-R) theory and Anchoring Adjustment Model (AAM); behavioral utility prediction affected by omission bias is quantified with Prospect Theory. Second, guided by SAB-TDM, a dynamic Bayesian network is used to assess HAIP safety. A case study on autonomous ship collision avoidance verifies effectiveness of the method. Results show that the above biases mutually contribute to seriously threaten HAIP safety. Full article

The development of Maritime Autonomous Surface Ships (MASS) has seen significant advancements in recent years, yet there remains a lack of comprehensive studies that holistically address the architecture of autonomous navigation systems and explain the complexity of their individual elements. This paper aims to bridge this gap by conducting a literature review that consolidates key research in the field and presents a detailed architecture of autonomous navigation systems. The results of this study identify several major clusters essential to MASS navigation architecture, including (1) autonomous navigation architecture, (2) decision-making and action-taking system, (3) situational awareness and associated technologies, (4) sensor fusion technology, (5) collision avoidance subsystems, (6) motion control and path following, and (7) mooring and unmooring. Each cluster is further dissected into sub-clusters, highlighting the intricate and interdependent nature of the components that facilitate autonomous navigation. The implications of this study are vital for multiple stakeholders. Ship captains and seafarers must be prepared for new navigation technologies, while managers and practitioners can use this architecture to better understand and implement these systems. Researchers will find a foundation for future investigations, particularly in filling knowledge gaps related to autonomous ship operations. This study makes a substantial contribution by filling a critical gap in the maritime literature, offering a detailed explanation of the elements within autonomous navigation systems. Full article

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments. Full article

The rapid growth of offshore wind farms (OWFs) as renewable energy sources has heightened concerns about maritime traffic safety and management in high-density traffic zones. These areas, characterized by complex interactions among diverse ship types and spatial constraints, require advanced situational awareness to prevent collisions and ensure efficient operations. Traditional maritime traffic systems often lack the granularity to assess the multifaceted risks around OWFs. Existing research has explored local traffic patterns and collision risks but lacks comprehensive frameworks for evaluating traffic complexity at both micro and macro levels. This study proposes a new complexity assessment model tailored to OWF areas, integrating micro-level ship interactions and macro-level traffic flow conditions to capture a holistic view of traffic dynamics. Using extensive historical AIS data from the Yangtze River Estuary, the model evaluates the impact of the proposed OWF on existing traffic complexity. The results demonstrate that OWFs increase navigational complexity, particularly in route congestion, course adjustments, and encounter rates between ships. Different ship types and sizes were also found to experience varying levels of impact, with larger ships and tankers facing greater challenges. By providing a quantitative framework for assessing traffic complexity, this research advances the field’s ability to understand and manage the risks associated with OWFs. The findings offer actionable insights for maritime authorities and OWF operators, supporting more effective traffic management strategies that prioritize safety and operational efficiency in high-density maritime areas. Full article

Detecting maritime ship targets in complex aerial images is significantly challenging due to the relatively small size of the ship compared to the image’s predominant background. Current methodologies used to identify maritime targets in aerial imagery are susceptible to disruption from intricate backgrounds, leading to misidentification, overlooked detection, and inaccurate target localization. To address these challenges, we proposed the maritime background suppression network (MBSDet), which facilitates the reliable detection of multi-scale targets in intricate backgrounds. Firstly, we developed a background suppression (BS) module that integrates the Transformer’s context-aware capabilities with local attention features to enhance the model’s ability to identify multi-scale objects. Secondly, we present the multidimensional feature enrichment (MFE) module, which employs feature pyramid-based rearrangement modules and dynamic upsampling operators instead of conventional upsampling methods to enhance feature richness while attenuating background features. The mean average precision (mAP) values can reach 90.54% and 88.89% on HRSC2016 and DOTA v1.0 datasets, respectively. The experimental results indicate that MBSDet exhibits outstanding detection and localization accuracy for objects in difficult background situations. Full article

We examine the current state of the art and the related research on the automated detection and tracking of small objects—or persons—in the context of a person-overboard (POB) scenario and present the associated governing relationship between different technologies, platforms, and approaches as a system of systems. A novel phase model, structuring a POB scenario, comprises three phases: (1) detection, (2) search and track, and (3) rescue. Within these phases, we identify the central areas of responsibility and describe in detail the phases (1) and (2). We emphasize the importance of a high-level representation of different systems and their interactions to comprehensively represent the complexity and dynamics of POB scenarios. Our systematic classification and detailed description of the technologies and methods used provide valuable insights to support future regulatory and research activities. Our primary aim is to advance the development of corresponding technologies and standards. Full article

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness. Full article

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships. Full article

With the development of seaborne trade, international maritime crime is becoming increasingly complex. Detecting maritime threats by fusing the physical movement data from traditional physical sensors is not sufficient. Thus, soft data, including intelligence reports and news articles, need to be incorporated into the situational awareness models of maritime threats. In this regard, this study developed an automated construction method of knowledge graphs for pirate events, which lays a foundation for subsequent maritime threat reasoning and situational awareness. First, a knowledge graph ontology model for pirate events was designed. Secondly, the BERT-BiLSTM-CRF model is proposed for named-entity recognition, and an entity linking algorithm based on distant learning and context attention mechanism is proposed to remove the conceptual ambiguity. Thirdly, based on traditional distant supervision relation extraction, which is based on sentence-level attention mechanism, bag-level and group-level attention mechanism methods are additionally proposed to further enhance the performance of distant supervision relation extraction. The proposed model demonstrated high performance in named-entity recognition, entity linking, and relation extraction tasks, with an overall F1-score of over 0.94 for NER and significant improvements in entity linking and relation extraction compared to traditional methods. The constructed knowledge graphs effectively support maritime threat reasoning and situational awareness, offering a substantial contribution to the field of maritime security. Our discussion highlights the model’s strengths and potential areas for future work, while the conclusion emphasizes the practical implications and the readiness of our approach for real-world applications. Full article

PURPOSE: To highlight the degree of perception among the young population in Romania (ages 18–35) regarding the impact of the decisions by some European Union (EU) countries to delay the full acceptance (air, maritime, and land) of Romania’s accession to the Schengen Area on various indicators characterizing the country’s sustainable development, national security interests, and the quality of life of the population. This study was deemed timely in light of the EU’s decision regarding the removal of air and maritime border controls with Romania starting on 31 March 2024 (“Romania’s accession to the Schengen Area, air and maritime”), while maintaining controls at land borders (non-acceptance of “land accession”), under the conditions of Romania fully meeting all the requirements imposed by European legislation, a situation considered by national authorities and the Romanian public as discrimination compared to European states, and causing significant disadvantages in terms of sustainable development and the country’s security interests. METHODS: The study was based on a questionnaire administered to 785 Romanian citizens aged 18–35 years. Data were collected during the period from 15 March 2024 to 15 April 2024, centered around the date of 31 March 2024, which marks “Romania’s accession to the Schengen Area, air and maritime”, through the elimination of EU air and maritime border controls with Romania. The main method used was statistical analysis (descriptive, bivariate, and multivariate), focused on detecting and assessing the degree of respondents’ awareness regarding the efforts of authorities for the full accession of Romania to the Schengen Area and the EU’s response to this effort; the negative impact of delaying the elimination of controls at the EU’s land borders with Romania (“land accession”) on national security interests, on the sustainable development of the country by relating to the objectives of the 2030 Sustainable Development Agenda developed at the United Nations Summit in September 2015, and on the quality of life of people in Romania. Additionally, the study was based on empirical research of the analyzed issues, in accordance with the available literature. RESULTS: The study reveals that, in the context of a very high level of awareness among respondents regarding the efforts of national authorities for Romania’s accession to the Schengen Area (75.26–91.30%) and access to credible information resources and materials about these efforts (65.10–73.05%), Romania’s status as a full-fledged EU member is a determining factor for motivating Romania’s full accession to the Schengen Area (83.33–93.48%). Furthermore, the decisions of some European states to delay full accession are perceived as subjective/unfair actions that are likely to limit/violate the access/facilities of Romanian citizens, as full-fledged EU citizens, to the values of the European democratic space (59.12–76.69%). Additionally, respondents believe that these decisions are likely to affect Romania’s security interests (43.61–56.52%), exacerbate the discrepancies between the living standards of Romania’s population and those of Western European countries (47.59–71.73%), and negatively impact the national implementation of the 2030 Sustainable Development Agenda objectives, as these objectives mostly target significant national infrastructures. Moreover, the WHOQOL-BREF measurement tool shows that these decisions negatively affect, to a large and very large extent, the quality of life of people in Romania (32.07–41.31%), with specific characteristics of the investigated domains (“Physical”, “Psychological”, “Environmental”, and “Social”), depending on the considered socio-demographic variables. CONCLUSIONS: We consider that the study conducted provides a scientifically documented information base regarding respondents’ awareness of Romania’s efforts to join the Schengen Area, their perception of the direct consequences on national security, sustainable development of Romania, and quality of life, as a result of the decisions by some EU countries to delay Romania’s full accession to the Schengen Area. Lastly, the study reveals the correlation between the dissatisfaction/frustration of the population caused by the decision to delay Romania’s full accession to the Schengen Area and the questioning of the social, economic, political, and security consequences induced by this decision. Full article

In response to the evolving landscape of maritime operations, new technologies are on the horizon as mixed reality (MR), which shall enhance navigation safety and efficiency during remote assistance as, e.g., in the remote pilotage use case. However, up to now, it is uncertain if this technology can provide benefits in terms of usability and situational awareness (SA) compared with screen-based visualizations, which are established in maritime navigation. Thus, this paper initially tests and assesses novel approaches to pilotage in the congested maritime environment, which integrates augmented reality (AR) for ship captains and virtual reality (VR) and desktop applications for pilots. The tested prototype employs AR glasses, notably the Hololens 2, to superimpose the Automatic Identification System (AIS) data directly into the captain’s field of view, while pilots on land receive identical information alongside live 360-degree video feeds from cameras installed on the ship. Additional minimum functionalities include waypoint setting, bearing indicators, and voice communication. The efficiency and usability of these technologies are evaluated through in situ tests conducted with experienced pilots on a real ship using the System Usability Scale, the Situational Awareness Rating Technique, as well as Simulator Sickness Questionnaires during the assessment. This includes a first indicative comparison of VR and desktop applications for the given use case. Full article

Cybersecurity is becoming an increasingly important aspect in ensuring maritime data protection and operational continuity. Ships, ports, surveillance and navigation systems, industrial technology, cargo, and logistics systems all contribute to a complex maritime environment with a significant cyberattack surface. To that aim, a wide range of cyberattacks in the maritime domain are possible, with the potential to infect vulnerable information and communication systems, compromising safety and security. The use of navigation and surveillance systems, which are considered as part of the maritime OT sensors, can improve maritime cyber situational awareness. This survey critically investigates whether the fusion of OT data, which are used to provide maritime situational awareness, may also improve the ability to detect cyberincidents in real time or near-real time. It includes a thorough analysis of the relevant literature, emphasizing RF but also other sensors, and data fusion approaches that can help improve maritime cybersecurity. Full article

Intelligent navigation is a crucial component of intelligent ships. This study focuses on the situational awareness of intelligent navigation in inland waterways with high vessel traffic densities and increased collision risks, which demand enhanced vessel situational awareness. To address perception data association issues in situational awareness, particularly in scenarios with winding waterways and multiple vessel encounters, a method based on trajectory characteristics is proposed to determine associations between Automatic Identification System (AIS) and radar objects, facilitating the fusion of heterogeneous data. Firstly, trajectory characteristics like speed, direction, turning rate, acceleration, and trajectory similarity were extracted from ship radar and AIS data to construct labeled trajectory datasets. Subsequently, by employing the Support Vector Machine (SVM) model, we accomplished the discernment of associations among the trajectories of vessels collected through AIS and radar, thereby achieving the association of heterogeneous data. Finally, through a series of experiments, including overtaking, encounters, and multi-target scenarios, this research substantiated the method, achieving an F1 score greater than 0.95. Consequently, this study can furnish robust support for the perception of intelligent vessel navigation in inland waterways and the elevation of maritime safety. Full article

The Polar Operational Limit Assessment Risk Indexing System (POLARIS) has been established as a viable framework for assessing operational capabilities and associated risks in polar waters. Despite its inherent suitability for high-latitude territories, ships navigating through seasonal ice-infested waters at lower latitudes also encounter critical safety, environmental, and economic issues exacerbated by the presence of ice. This necessitates a reliable and adaptable methodology that can serve as a reference for devising effective countermeasures. This study evaluated the use of POLARIS in the intricate ice conditions prevalent in the northern navigable waters (channels and anchorages) within Liaodong Bay of the Bohai Sea, located at relatively low latitudes. Using GF-4 satellite imagery, ice conditions were collected, and the POLARIS methodology was employed to calculate Risk Index Outcome (RIO) values for non-ice-strengthened vessels during the winter season of 2021–2022. The results showed that sectors 3, 4, 5, 7, 9, 10, and 11 within the northern part of Liaodong Bay exhibited a higher risk, with sectors 5 and 10 exhibiting the most significant risk, while sectors 1 and 2 demonstrated relatively lower risk levels. The concurrence of these findings with acknowledged ice patterns and local maritime practices confirms the applicability of the POLARIS methodology in saline, seasonally ice-covered seas. Notably, the combination of POLARIS with high-resolution satellite imagery facilitated a more precise and rapid assessment of ice risk, thereby enhancing situational awareness and informing decision-making processes in maritime operations under icy conditions. In addition, this study provides preliminary evidence that POLARIS is suitable for fine-scale scenarios, in addition to being applicable to sparse-scale scenarios, such as polar waters, especially with high-resolution ice data. At the same time, this study highlights the potential of POLARIS as a disaster prevention strategy and a tool for the maritime industry to address ice challenges. Full article