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Ships connect the global economy through maritime transport. However, their susceptibility to increasing geopolitical conflicts has heightened concerns about the risks to crew safety and navigation security. This systematic literature review (SLR), utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, rigorously examines the safety and security of autonomous ships in maritime transport. The methodology employs a comprehensive search across major databases including Scopus, Google Scholar, and Science Direct, based on explicit inclusion criteria focusing on recent advancements from 2014 to 2023. By methodically analyzing 58 relevant publications screened from an initial pool of 1407, this paper highlights critical trends and gaps in the application of advanced sensor technologies, cybersecurity measures, and autonomous navigation systems. The findings provide insights into the operational challenges and technological developments shaping the future of maritime safety and security, offering valuable guidance for policymakers and industry stakeholders. This research contributes to scholarly discourse in this industry by mapping the trajectory of technological integration and its implications for maritime operations in a global context.

This paper proposes and demonstrates a simulator-based approach for testing and assessing human operators' ability and performance in supervising autonomous ships. In the autonomy concept studied here, it is assumed that the autonomous navigation system is capable of detecting and notifying the human operator prior to entering a challenging situation. The system will attempt to resolve the situation with a proposed evasive maneuver, but the system may occasionally make errors or select sub-optimal solutions. When the human operator is notified about a challenging situation, the operator should closely assess the situation, and intervene if (and only if) necessary. The proposed approach allows us to test and quantify the human operators' abilities and performances in supervising an autonomously navigated ship. The approach is demonstrated on 56 simulator experiments, involving seven different navigators that perform eight different traffic scenarios. The scenarios are all based on real traffic data collected from a Norwegian ferry-crossing. The demonstration shows that the candidates are successful in supervising the autonomous ship in low-complexity traffic scenarios where it is easy for the operator to interpret the system's decisions. The operators' ability to intervene when and only when needed does, however, decline in more complex scenarios, and their performance is highly dependent on the traffic scenario and vary significantly between the candidates. [ABSTRACT FROM AUTHOR]

Ships connect the global economy through maritime transport. However, their susceptibility to increasing geopolitical conflicts has heightened concerns about the risks to crew safety and navigation security. This systematic literature review (SLR), utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, rigorously examines the safety and security of autonomous ships in maritime transport. The methodology employs a comprehensive search across major databases including Scopus, Google Scholar, and Science Direct, based on explicit inclusion criteria focusing on recent advancements from 2014 to 2023. By methodically analyzing 58 relevant publications screened from an initial pool of 1407, this paper highlights critical trends and gaps in the application of advanced sensor technologies, cybersecurity measures, and autonomous navigation systems. The findings provide insights into the operational challenges and technological developments shaping the future of maritime safety and security, offering valuable guidance for policymakers and industry stakeholders. This research contributes to scholarly discourse in this industry by mapping the trajectory of technological integration and its implications for maritime operations in a global context. [ABSTRACT FROM AUTHOR]

The smart shipping and maritime technology encompass blockchain and smart contracts technology, information perception technology, intelligent decision-making technology, intelligent routing technology, marine communication network security technology, route planning technology, autonomous navigation technology, state monitoring and fault diagnosis technology and so on. Nevertheless, these advancements bring practical and legal challenges, as well as a new threat in the digital age: cybercrime. This paper deals with the key challenges and opportunities associated with the integration of digital technologies in shipping and transport such as smart contracts and unmanned ships, and how do cyber issues impact the safety, security, and efficiency of maritime operations. The first part examines the technological background into which smart contracts are integrated and operate, i.e. the terms blockchain and technology distributed ledger technology. The concept, the mechanism and the types of smart contracts in the maritime industry are further analysed. Also, the aspects of new technologies such as Autonomous Vessels and the challenges they raise are examined. The second part focuses on the legal potential of smart contracts examining their issues and the cyber challenges in the maritime industry. The critical remarks and conclusions drawn are listed at the end of the paper. [ABSTRACT FROM AUTHOR]

The maritime industry is undergoing a profound transformation with the integration of autonomous technologies, which brings new challenges and opportunities for the education and training of seafarers. This article aims to examine the evolving landscape of autonomous ships and its impact on maritime education, with a focus on the changing roles and responsibilities of seafarers. The levels of autonomy defined by the International Maritime Organization (IMO) provide a framework for understanding the evolution towards fully autonomous ships and highlight the changing roles and responsibilities of seafarers. Using a systematic review based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA), this study examines maritime education for maritime autonomous surface ships (MASS). Using Scopus, Web of Science (WoS) and Google Scholar, a comprehensive search was conducted to identify relevant studies focusing on seafarer training and the impact of automation in the maritime sector. The analysis included bibliometric assessments, historical reviews and a categorization of research topics. This systematic review contributes to a deeper understanding of the current state and trends in maritime education for autonomous shipping. The findings inform educators and industry stakeholders about the critical aspects of education and training needed to address the challenges and realize the potential benefits of autonomous technologies in the maritime sector. The inclusion of bibliometric analysis enriches the study by providing a comprehensive overview of the researchers. [ABSTRACT FROM AUTHOR]

In navigation applications, Artificial Intelligence (AI) can improve efficiency and decision making. It is not clear, however, how designers should account for human cooperation when integrating AI systems in navigation work. In a novel empirical study, we examine the transition in the maritime domain towards higher levels of machine autonomy. Our method involved interviewing technology designers (n = 9) and navigators aboard two partially automated ferries (n = 5), as well as collecting field observations aboard one of the ferries. The results indicated a discrepancy between how designers construed human-AI collaboration compared to navigators' own accounts in the field. Navigators reflected upon their role as one of 'backup,' defined by ad-hoc control takeovers from the automation. Designers positioned navigators 'in the loop' of a larger control system but discounted the role of in-situ skills and heuristic decision making in all but the most controlled takeover actions. The discrepancy shed light on how integration of AI systems may be better aligned to human cooperation in navigation. This included designing AI systems that render computational activities more visible and that incorporate social cues that articulate human work in its natural setting. Positioned within the field of AI alignment research, the main contribution is a formulation of human-AI interaction design insights for future navigation and control room work. [ABSTRACT FROM AUTHOR]

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution. [ABSTRACT FROM AUTHOR]

During the last few years, autonomous and unmanned vessels have become technologically more advanced, and their imminent inclusion as permanent participants in marine traffic is more feasible. Nevertheless, international maritime law is still not adjusted to the evolving reality at sea and represents a regulatory barrier for their worldwide operation. This article focuses on the specific problem of navigational safety in light of the collision regulations under the 1972 Convention on the International Regulations for Preventing Collisions at Sea (1972 COLREG). It emphasizes that the 1972 COLREG—which has long been criticized—also requires amendments in the context of conventional vessels. Thus, it is argued that the revision of selected aspects of the 1972 COLREG aiming to update it for conventional ships would also be an opportunity to accommodate the new categories of vessels therein. [ABSTRACT FROM AUTHOR]

Copyright of AIN Journal is the property of Arab Institute of Navigation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACTOngoing advancements in collision avoidance systems (CAS) for autonomous ships, built on diverse algorithms, have significantly enhanced performance. These systems, underpinned by a range of sophisticated algorithms, demonstrate increased accuracy in detecting potential collisions, quicker response times, and more reliable path prediction and avoidance maneuvers. Nevertheless, a prevalent trend in these systems is the insufficient consideration of the broader behavioral context of maritime navigation, especially for manned ships. The objective of this research is to present a methodology that can improve the capabilities of CAS in autonomous ships based on target ships’ maneuvering behaviors. This methodology will enable autonomous ships to respond adaptively to the actions of target ships, particularly manned ships, while also reacting to immediate environmental stimuli. The ultimate goal is to enhance the safety of navigation. This research utilizes information derived from simulated ship handling training, focusing on feature extraction within steering, spatial, and temporal elements. The tests prioritize the “Average Distance Deviation” metric for designing test scenarios. Simulation-based evaluation tests feature a velocity obstacle algorithm that discerns CAS action in single-ship crossing situations. The research reveals the potential of integrating behavioral dynamics into CAS, which enhances safety in autonomous maritime navigation and fosters a more integrated approach, where autonomous and manned ships coexist synergistically in shared environments.

Past and ongoing research in the design, development, and implementation of fully autonomous and unmanned ships has revealed operational, environmental, and financial benefits for the maritime industry. However, with the benefits of being highly intuitive and intelligent systems, there are risks of mistakes and failures caused by their operators i.e. the unavoidable human element. With predictions of both seafarers and non-seafarers to be involved in the critical operations of autonomous vessels, it was imperative to identify key maritime stakeholders and conduct research which would investigate their beliefs and perceptions on the training requirements of the future shore-based operators. The key maritime stakeholders were 37 participants who were a mix of seafarers, maritime regulators, maritime education and training providers, and persons involved in other facets of the maritime business. The qualitative research involving in-depth interviews with the participants provided key insights which helped in identifying essential skills and competencies towards building a recommendatory framework which can be used as a basis to reform the Standards of Training, Certification, and Watchkeeping (STCW) Code in order to make seafarers future ready. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Commercial & Intellectual Property Law (TFM) / Ticaret ve Fikri Mülkiyet Hukuku Dergisi is the property of Ankara Yildirim Beyazit University, Facult of Law and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The inherent complexities of Artificial Intelligence (AI) and machine learning (ML) technologies expose autonomous ships to a wide range of multifaceted interconnected risks. However, very few studies have aimed at the holistic risk assessment of autonomous ships. To this end, this study employs an expert-opinion-based integrated machine learning approach amalgamating logistic regression and Bayesian network to conduct risk assessment for autonomous ships. The results reveal human factor interactions and operational issues as the prominent accident causation factors. The findings of this study will contribute significantly to the existing literature on autonomous ships and the complexities involved in their operational systems. By identifying critical factors causing accidents and their impact on autonomous ship safety and resilience, stakeholders such as autonomous ship manufacturers, port authorities, shipping companies, and governments can develop more efficient and effective operational and safety systems. [ABSTRACT FROM AUTHOR]

Education and training of seafarers are defined in the provisions of the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW Convention) and elaborated by the set of the International Maritime Organization (IMO) Model Courses. These documents represent minimum requirements for the education and training of seafarers at the international level. IMO member states shall integrate these requirements as appropriate into their maritime high school and college training programs and into training programs leading to STCW certification. Although there are different views in the literature and in practice about the existing seafarers training system, this system is standardized worldwide. This allows consistent acquisition of knowledge, skills, and competences for seafarers on merchant ships. The development and gradual introduction of autonomous ships raises numerous questions about the future trends of world shipping. Various researches are currently being conducted on autonomous ships. This technological progress brings with itself many challenges and one of them is the education of the personnel who will control and monitor these ships. It is clear that a new approach to seafarer's education needs to be developed. The development of this concept opens numerous questions from different segments of shipping and maritime security in general, particularly in the context of maritime cyber security. The paper analyses the education concept for seafarers in the Republic of Croatia, paying special attention to trends related to the development and deployment of Maritime Autonomous Surface Ship (MASS). [ABSTRACT FROM AUTHOR]

In the maritime industry, the systematic validation of collision avoidance systems of autonomous ships is becoming an increasingly important issue with the development of autonomous ships. The development of collision avoidance systems for autonomous ships faces inherent risks of programming errors and has mostly been tested in limited scenarios. Despite efforts to verify these systems through scenario testing, these scenarios do not fully represent the complex nature of real-world navigation, limiting full system verification and reliability. Therefore, this study proposed a method for analyzing collision risk situations extracted from AIS data through graph-based modeling and establishing validation scenarios. This methodology categorizes collision risk scenarios according to their centrality and frequency and demonstrates how simple collision risk situations gradually evolve into harsh situations. [ABSTRACT FROM AUTHOR]

Infrared ship detection is of great significance due to its broad applicability in maritime surveillance, traffic safety and security. Multiple infrared sensors with different spectral sensitivity provide enhanced sensing capabilities, facilitating ship detection in complex environments. Nevertheless, current researches lack discussion and exploration of infrared imagers in different spectral ranges for marine objects detection. Furthermore, for unmanned marine vehicles (UMVs), e.g., unmanned surface vehicles (USVs) and unmanned ship (USs), detection and perception are usually performed in embedded devices with limited memory and computation resource, which makes traditional convolutional neural network (CNN)-based detection methods struggle to leverage their advantages. Aimed at the task of sea surface object detection on USVs, this paper provides lightweight CNNs with high inference speed that can be deployed on embedded devices. It also discusses the advantages and disadvantages of using different sensors in marine object detection, providing a reference for the perception and decision-making modules of USVs. The proposed method can detect ships in short-wave infrared (SWIR), long-wave infrared (LWIR) and fused images with high-performance and high-inference speed on an embedded device. Specifically, the backbone is built from bottleneck depth-separable convolution with residuals. Generating redundant feature maps by using cheap linear operation in neck and head networks. The learning and representation capacities of the network are promoted by introducing the channel and spatial attention, redesigning the sizes of anchor boxes. Comparative experiments are conducted on the infrared ship dataset that we have released which contains SWIR, LWIR and the fused images. The results indicate that the proposed method can achieve high accuracy but with fewer parameters, and the inference speed is nearly 60 frames per second (FPS) on an embedded device.

The maritime industry is undergoing a profound transformation with the integration of autonomous technologies, which brings new challenges and opportunities for the education and training of seafarers. This article aims to examine the evolving landscape of autonomous ships and its impact on maritime education, with a focus on the changing roles and responsibilities of seafarers. The levels of autonomy defined by the International Maritime Organization (IMO) provide a framework for understanding the evolution towards fully autonomous ships and highlight the changing roles and responsibilities of seafarers. Using a systematic review based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA), this study examines maritime education for maritime autonomous surface ships (MASS). Using Scopus, Web of Science (WoS) and Google Scholar, a comprehensive search was conducted to identify relevant studies focusing on seafarer training and the impact of automation in the maritime sector. The analysis included bibliometric assessments, historical reviews and a categorization of research topics. This systematic review contributes to a deeper understanding of the current state and trends in maritime education for autonomous shipping. The findings inform educators and industry stakeholders about the critical aspects of education and training needed to address the challenges and realize the potential benefits of autonomous technologies in the maritime sector. The inclusion of bibliometric analysis enriches the study by providing a comprehensive overview of the researchers.

Maritime autonomous surface ships (MASSs) have received increased attention in both academic research and industrial applications in the global shipping and shipbuilding industry in recent years. According to its definition, a MASS can independently operate without human interaction. This brings the problem of how to test and evaluate the capabilities of the autonomous navigation control system developed for MASS. This paper proposes the novel concept of a variable stability ship and the design of its control system. A variable stability ship consists of a model-scaled ship, a model-following controller, and a hardware and software platform to implement its functions. The variable stability ship can replicate the dynamic responses of different reference ships despite their dissimilarities in physical parameters and hydrodynamic characteristics with the design of the model-following controller. Therefore, it can be used to test the performance of different control strategies on various ship types such that the R&D costs of physical model-based tests can be reduced, as well as shortening the time spent in developing physical ship models. Preliminary simulations are carried out to show its effectiveness and applicability. [ABSTRACT FROM AUTHOR]

Research on motion control systems for marine surface vessels has generated a vast academic literature and many industrial applications since the early 20th century. The recent focus on autonomous ships has sparked intensive research and innovation activities of dock-to-dock operations, including the final docking phase. Specifically, automated docking involves systems that enable vessels to dock safely, independently, and energy-efficiently at designated locations with a specific heading. Challenges include managing large sideslip angles, static and dynamic obstacles, and navigation in complex port geometries. Despite its complexity, the docking problem has received less attention compared to dynamic positioning or course-keeping systems. This paper therefore provides a thorough overview of the research on automated docking for marine surface vessels, from 1980 to June 2023. The paper introduces the Docking Characteristic Index (DCI) as a metric to identify the scope and approach of systems addressing various docking challenges, not as an evaluation of their overall quality. Using the calculated DCI-Scores, we discuss some of the works in more detail. The survey reveals a rising trend in publications and an increasing emphasis on experimental verification. Despite advancements, current methodologies exhibit trade-offs and limitations, particularly in handling dynamic obstacles, robustness against external forces, and situational awareness. The paper identifies the need for more comprehensive and integrated solutions to these challenges. As the demand for fully autonomous operations grows, the results suggest that future research should focus on developing holistic and robust docking strategies, that are verified experimentally, to achieve safe, efficient, and effective automated docking systems.

The third Naval Engineering and Ship Control special edition of the Journal of Marine Engineering and Technology aims to present cutting-edge research on naval engineering and ship control, as was presented during the International Naval Engineering Conference and Exhibition (INEC) and the International Ship Control Systems Symposium (iSCSS), held together in Delft in 2022. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time monitoring and remote control (for unexpected circumstances) from land; this poses a potential cyberthreat to various data collected inside and outside the ships and to the applied AI technology. For the safety of autonomous ships, cybersecurity around AI technology needs to be considered, in addition to the cybersecurity of the ship systems. By identifying various vulnerabilities and via research cases of the ship systems and AI technologies, this study presents possible cyberattack scenarios on the AI technologies applied to autonomous ships. Based on these attack scenarios, cyberthreats and cybersecurity requirements are formulated for autonomous ships by employing the security quality requirements engineering (SQUARE) methodology. [ABSTRACT FROM AUTHOR]

Autonomous ships, also known as maritime autonomous surface ships (MASS), are vessels that use artificial intelligence and robotics technologies to navigate independently. Due to their advanced technological capabilities, MASS is expected to play a significant role in the future of the shipping industry. The International Maritime Organization (IMO) is currently developing international standards for MASS classification, including accident avoidance technologies. However, the issue of how to mitigate the consequences of accidents involving autonomous ships has not been sufficiently addressed. Therefore, this study focuses on alternative design solutions and emergency response systems for MASS to properly control emergency situations and minimize the impact of accidents, such as flooding and fire on board. The goal is to efficiently address such accidents, especially in situations where the number of people on board is significantly reduced or the ship is being operated automatically or remotely, and to promptly detect and respond to such situations from a remote location. This paper investigates the possibility of modifying the design of the air conditioning system to delay flooding by considering a reduction in the number of crew members in order to prevent flooding. To prevent the spread of fire, the study examines early blockage measures for areas where air can enter. Flood and fire response systems were configured to be executed manually or automatically based on the results of presimulated scenarios defined in thousands of accident scenarios. Each accident propagation and response situation proposes an alternative using a coded shortcut key utilizing graphic symbols of international standard ISO 23120. [ABSTRACT FROM AUTHOR]

The recent maiden voyage of the Yara Birkeland witnessed yet another development in autonomy that is transforming the maritime sector. Marine autonomous surface ships (MASS) are claimed to bring many opportunities to society at large, not least in terms of operational efficiency and safety of the crew, fewer emissions, and greener shipping. On the assumption that MASS will prove safe enough to ply our seas and oceans, this article investigates the flexibility and ability of the existing International Maritime Organization (IMO) regulations on ships' routing, ship reporting, and vessel traffic service (VTS) to respond to the technological developments, allowing for the operation of both remotely controlled ships without seafarers on board and fully autonomous ships. It argues that the regulations in question are largely supportive of autonomy. Challenges, however, exist when it comes to the employment of fully autonomous ships and the effective use of VTS. [ABSTRACT FROM AUTHOR]

Safe navigation for maritime autonomous surface ships (MASS) is a challenging task, and generally highly dependent on effective collaboration between multiple sub-systems in environments with various levels of uncertainty. This paper presents a novel methodology combining risk-based optimal control and path following with autonomous machinery management (AMM) for MASS navigation and supervisory risk control. Specifically, a risk-aware particle swarm optimization (PSO) scheme utilizes "time-to-grounding" predictions based on weather data and electronic navigational charts (ENC) to simultaneously control both the ship's motion as well as the machinery system operation (MSO) mode during transit. The proposed autonomous navigation system (ANS) is comprised of an online receding horizon control that uses a PSO approach from previous works, which produces a dynamic risk-aware path with respect to grounding obstacles from a pre-planned MASS path, subsequently given as the input to a line-of-sight guidance controller for path following. Moreover, the MSO mode of the AMM system is simultaneously selected and assigned to explicit segments along the risk-aware path throughout the receding horizon, which effectively introduces into the optimization scheme an additional safety layer as well as another dimension for risk or resource minimization. The performance of the resulting ANS is demonstrated and verified through simulations of a challenging scenario and human assessment of the generated paths. The results show that the optimized paths are more efficient and in line with how human navigators would maneuver a ship close to nearby grounding obstacles, compared to the optimized paths of selected previous works. [ABSTRACT FROM AUTHOR]

The United Nations Convention on the Law of the Sea of 1982 (UNCLOS) and the International Maritime Organization (IMO) are working on the regulation of autonomous surface maritime vessels (MASS). The IMO has adopted a preliminary definition of MASS and has established a roadmap for the adoption of a non-binding code in 2024 and a binding code in 2025. Negotiations began in 2022 to adapt existing regulations and ensure maritime safety. The development of a new instrument, a MASS Code, is proposed to address common issues and potential gaps in the regulation of autonomous vessels. [Extracted from the article]

This article considers the current technological developments in uncrewed Maritime Autonomous Surface Ships (MASS), examines the legal and regulatory challenges that they raise, and describes the ways in which an international body (the International Maritime Organization, IMO) and a national agency (UK Maritime and Coastguard Agency, MCA) are engaging with the massive task of regulating this new development in shipping. To achieve the research aim, the article combines primary data from interviews with industry, government and international regulators, a survey completed by practitioners, experts and academics working in the field, and relevant secondary sources. The article makes an original contribution by noting and analyzing the approaches of the IMO and the UK government–bodies which are highly influential in shaping global attitudes, preparation, adoption and responses to such innovative technologies like MASS–and how they have engaged with the difficult question of regulating the various legal, technical and cybersecurity challenges that they raise. It concludes that for the MASS technology to fulfill its potential and gradually see the integration of such vessels in international shipping in a safe, secure and sustainable way, the international community needs to work together, and update by consensus the key legal instruments and policy documents. In this major task, the UK approach and IMO's leading role can play a protagonist role in developing best practice and implementing respectively.

In the maritime industry, the systematic validation of collision avoidance systems of autonomous ships is becoming an increasingly important issue with the development of autonomous ships. The development of collision avoidance systems for autonomous ships faces inherent risks of programming errors and has mostly been tested in limited scenarios. Despite efforts to verify these systems through scenario testing, these scenarios do not fully represent the complex nature of real-world navigation, limiting full system verification and reliability. Therefore, this study proposed a method for analyzing collision risk situations extracted from AIS data through graph-based modeling and establishing validation scenarios. This methodology categorizes collision risk scenarios according to their centrality and frequency and demonstrates how simple collision risk situations gradually evolve into harsh situations.

Since the rise of intelligent control and multi-sensor integration technology, the development of autonomous ships has been significantly growing over the last decade. This advancement has painted a picture of extreme change with a radical alteration of human factors and new operating models. Inherent with the development of such ships, some concerns regarding their safe operation and integration into the maritime regulatory framework arose. Although the introduction of autonomous vessels is not an impending factor, it is the future, and one day will come into application. The primary concern inherent in the development of autonomous ships is compliance with the current International Regulations for Preventing Collisions at Sea (COLREGS) 1972. This paper uses an interdisciplinary approach to examine autonomous vessel seaborne interactions. The results show that we should actively support the modernization of the maritime industry and integrate it with other autonomous industries in the world. [ABSTRACT FROM AUTHOR]

Autonomous and unmanned shipping are currently hot topics in the maritime industry. However, there are many different views on how the ultimate goal of an unmanned, autonomous ship will be reached. On any given ship, a large range of tasks is performed every day, each of which need to be replaced in such a way that no human presence is required on board. In this article, different possible combinations of tasks to be replaced are explored systematically, leading to an overview of the most beneficial combinations of tasks to replace together and a logical sequence in which to replace them. This leads to a plausible implementation path from low-manned ships towards fully unmanned autonomous ships. [ABSTRACT FROM AUTHOR]

The ability to effectively process large amounts of information in reasonable time will be important for robust deliberative collision avoidance (COLAV) planning algorithms. Failure to do so can lead to collision, and can be compared to lack of proper supervision from officers on watch (OOW). The main contribution in this article is a parallelized implementation of the Probabilistic Scenario-Based Model Predictive Control (PSB-MPC) on a Graphical Processing Unit (GPU) platform which incorporates both dynamic obstacle avoidance and anti-grounding. Simulation results demonstrate that the COLAV planner can produce collision-free trajectories with respect to grounding hazards and nearby vessels at relatively low computational cost, and which also comply to the COLREGS when deemed possible. Corresponding run-time results show that the algorithm utilizing parallel processing performs better than the alternative for increasing numbers of own-ship control behaviours, nearby static and dynamic obstacles, and dynamic obstacle prediction scenarios considered.

Autonomous control and high-level decision-making for autonomous ships in complex environments are largely dependent on real-time navigational data from sensory systems and nautical charts. Visualization and manipulation of data contained within electronic navigational charts (ENC) or hydro/geographic information systems (HIS/GIS) is generally handled on the application or operations level. However, accessible and open-source application programming interfaces (API) for displaying and managing spatial bathymetry or other related sea-faring data for research and development are scarce. This work presents an open-source ENC visualization and manipulation API implemented in Python, with heavy emphasis on accessibility and simplicity. The current version of the package provides tools for displaying marine polygon data such as ships, ocean depths, reefs, and shallows, using the transverse Mercator projection. Additionally, polygon- and point-based transformation and calculation methods for application development based on spatial geometry, path planning and numerical optimal control are implemented. Usage of the spatial methods are demonstrated by examples involving high-level path or trajectory planning, optimization, and assisted decision-making for autonomous and remote-controlled ships.

ABSTRACTThe maritime world is rapidly evolving, and the impact of new technologies on the reduction of manning onboard vessels, leading eventually to unmanned large ships, has taken huge steps forward. Autonomous cargo ships, also known as autonomous container ships or maritime autonomous surface ships, are ocean-going vessels that carry containers or bulk cargo with little or no human interaction. This paper seeks to explore the impact of autonomous ships on the shipping industry by comparing and finding feasibility with the industry 4.0. The primary and most useful features of autonomous ships have been identified. Moreover, the advantages of autonomous ships have been shown and explained with their impacts. Some upcoming challenges are also pointed out. Extreme situation scenario (like the COVID-19 scenario) has been briefly discussed. Finally, an overall SWOT analysis is conducted.

In order to creates a comprehensive control algorithm through incorporating human driving characteristics and decision-making processes into control systems, a human-like constraint-adaptive model predictive control (MPC) with risk-tunable control barrier functions (CBF) for autonomous ships is proposed, aiming to bridge the gap between control efficiency, safety, stability and energy saving. To the best of our knowledge, it is the first time to design constraint relaxation in MPC-CBF. A novel framework for designing controllers is introduced. Learning from human drivers, precise path tracking with high-frequency refreshing control rates and modification operations is not required once the departure and destination points are clearly identified. Instead, navigating within the navigational region and obeying safety constraints are focused on. Adjusting risk boundaries based on specific requirements to meet security constraints is allowed. To the best of our knowledge, this is also the first time to adjust the attenuation coefficient in risk-tunable CBF for ship navigating near shoals, glaciers, underwater fishingnets, and reefs, where the variable area of the restricted navigational waterway is influenced by tide, etc. When the attenuation coefficient is large, the under keel clearance is also large and vice versa. Three different scenarios were tested for different decisions in various stages and regions. The results demonstrate that in comparison to traditional MPC, this approach guarantees safety and reduces computational time costs. • An event triggered model predictive control for ship control and risk avoidance is proposed. • Navigation within the channel and compliance with safety constraints are ensured. • Risk-adjustable CBF based on attenuation coefficient adjustment is constructed for navigation. [ABSTRACT FROM AUTHOR]

• A hazard identification method suitable for autonomous ships was developed. • This method consists of system modeling and hazard identification process. • Structure and task diagrams, checklist and keywords are used in this method. • The applicability and suitability of the method was demonstrated. • The method can be used in all design phases to ensure minimal rework. There is great interest in the practical use of autonomous ships. Although it is important to identify and address serious hazards at the initial design stage when constructing complicated large-scale systems, it is difficult to capture the entire structure of such a system. To address this issue, in this study, we propose the structure model–based hazard identification (SMB-HAZID) method, which is suitable for performing hazard identification of autonomous ships. The primary feature of this method is performing hazard identification using structure and task (ST) diagrams, a checklist, and keywords. The method includes a modeling method that can describe the overall structure of the system together with each component's tasks, information necessary for task execution, and interactions among the components in an ST diagram. In addition, this paper presents an example of using this method to perform hazard identification of a hypothetical autonomous ship at the early design stage. Through this trial application, we confirmed that SMB-HAZID is suitable for analyzing hazards within an autonomous ship. In our future research, we will improve the proposed method by developing and incorporating a technique for quantifying or semi-quantifying risks corresponding to the identified hazards. [ABSTRACT FROM AUTHOR]