Maritime Autonomous Surface Ships Research Articles

A study on digital literacy and influencing factors among Chinese seafarers

Digital literacy and skills have become critical indicators of international competitiveness, gaining attention across all sectors. As digital transformation accelerates, the shipping industry, a pivotal center of global trade, faces new challenges. The advent of Maritime Autonomous Surface Ships (MASS) further heightens the demand for enhanced digital literacy among seafarers. This study, guided by UNESCO’s Digital Literacy Global Framework (DLGF) and incorporating specific digital skill requirements for seafarers alongside the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) regulations, developed a tailored survey to assess the digital literacy of Chinese seafarers. Using an online survey, responses from 321 seafarers were collected, with participants selected based on professional experience across various maritime positions. The study examines the impact of factors such as education level, maritime experience, job position, and IT training on digital literacy levels. Results indicate that, overall, seafarers’ digital literacy is moderate, though those who participated in IT training show enhanced competencies. However, the study’s focus on Chinese seafarers may limit the generalizability of the findings to the broader, multinational seafaring population. Further research with more diverse samples is necessary to improve the applicability of the results to global seafaring contexts.

Minimum jerk motion planning for maritime autonomous surface ships based on Bézier curves

Maritime collisions are a leading cause of accidents at sea, causing severe casualties, economic losses, and environmental damage. Although numerous methods have been developed to mitigate collisions, existing research primarily emphasises decision-making and path planning, often overlooking motion planning methods that incorporate dynamic constraints and ship-specific kinematics. This paper presents a motion planning method that takes into account ship dynamics to minimise jerk, the acceleration change rate, during ship acceleration and deceleration. The paper demonstrates the differential flatness property of the 3-DOF ship motion model, which allows for the direct use of flat outputs to implement motion planning and facilitate the addition of dynamic constraints. The front-end path search is performed using the traditional A* algorithm. To represent the trajectory as a piecewise Bézier curve, we use the Bernstein polynomial basis function. Based on this curve, we propose a motion planning method that employs jerk as the cost function and includes necessary constraints. This transforms the problem into a typical convex quadratic programming problem, ensuring the solvability of the optimisation problem. Finally, simulations are carried out to evaluate the effectiveness of the proposed method.

The development of algorithms for safe control of an autonomous ship

The paper presents the results of a research on the development of algorithms for ship’s safe ship trajectory calculation and automatic control of the ship along the determined trajectory. These methods are intended for use in the autonomous navigation system of an Unmanned Surface Vehicle (USV) or a Maritime Autonomous Surface Ship (MASS). The work presents the consecutive stages of a research that must be carried out in order to develop a system that will perform the task of multidimensional ship control in a port. The safe trajectory is calculated with the use of the Ant Colony Optimization (ACO) method. The ship motion control is based on the Linear Matrix Inequalities (LMI) method, implemented using the Mathworks and Yalmip libraries. The results of controlling the ship’s motion in accordance with the calculated trajectory are presented in the paper. With the use of the developed system, the ship can move autonomously based on the information from the DGPS system and a gyrocompass. The presented results concerned a computer simulation of maneuvers in the port of the Blue Lady ship from the Foundation for Shipping Safety and Environmental Protection.

A human factor analysis of remote-control failure for maritime autonomous surface ships

Human factor management is crucial to the safety of maritime shipping. Whilst there has been extensive research on human factors in conventional shipping, the development of autonomous shipping necessitates the investigation of emerging human factors under the evolving landscape. Given that the research on human factor analysis for remote operation failure is limited, this study aims to enrich the literature by establishing a comprehensive human factor framework and empirically assessing the factors. Based on the literature review and expert interview, a total of ten categories and thirty-one human factors are organized under the human factor analysis and classification system. Adopting the purposive sampling, questionnaire surveys are then distributed to experts specializing in ship operations and automation to collect their degree of belief on potential human factors. A hybrid method composed of evidential reasoning and the rule-based Bayesian network is applied to analyze the data. The empirical results indicate that adverse mental states and adverse cognitive states are perceived to be the most threatening risk categories in remote operations. This study expands the academic research on human factor analysis and highlights the significance of managing psychological and cognitive factors. The findings provide managerial implications for the future safety management of autonomous ships.

A multi-phase mission success evaluation approach for maritime autonomous surface ships considering equipment performance degradation and system composition changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASSs). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML)-based algorithms, and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCCs) by using real-time data to optimise their operations, enhance safety, and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity vulnerabilities must be considered and properly addressed to prevent such complex systems from being tampered with. This paper proposes an approach that operates on two different levels to address cybersecurity. On one side, our solution is intended to secure communication channels between the SCCs and the vessels using Secure Exchange and COMmunication (SECOM) standard; on the other side, it aims to secure the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly and intrusion detection. The proposed approach is validated against a real implementation of the SCC deployed in the Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—2nd Edition

This Special Issue, entitled “Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—2nd Edition”, is the sequel to “Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships”, including five contributions [...]

A Risk Identification Method for Ensuring AI-Integrated System Safety for Remotely Controlled Ships with Onboard Seafarers

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Letting losses be lessons: Human-machine cooperation in maritime transport

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

A study on improving the international regulations for preventing collisions at sea (COLREG) for the introduction of maritime autonomous surface ships (MASS)

ABSTRACT Maritime Autonomous Surface Ships (MASS) have emerged with the advancements in information and communication technology (ICT), artificial intelligence (AI), and automation technology. MASS offers several advantages, such as reducing maritime accidents, increasing operational efficiency, lowering labour costs, and minimizing human errors. However, there are numerous challenges to address for the practical operation of MASS. In particular, the International Regulations for Preventing Collisions at Sea (COLREG), which governs the navigation rules for vessels, is a crucial international convention for the safety of navigation of MASS at sea. Therefore, this study examines the amendments to COLREG considering the characteristics of MASS. The International Maritime Organization (IMO) has defined various degrees of autonomy for MASS, ranging from full autonomy to remote control, and through the Regulatory Scoping Exercise (RSE), it has identified the need to amend certain provisions of COLREG to ensure the safe operation of MASS. Accordingly, this paper analyzes the results of the RSE conducted by IMO and proposes measures for improving COLREG to ensure the safe navigation of MASS.

MASS and COLREGS–Adaptability, Challenges and Coping Strategies

Discussions surrounding Maritime Autonomous Surface Ships (MASS) are occurring in academia, industry, and the International Maritime Organization (IMO). Automatic collision avoidance, as one of the key technologies of MASS, is the core of the autonomous navigation function of MASS, and its role is to solve the autonomous collision avoidance problem during MASS navigation. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is a fundamental basis for automatic collision avoidance, and the navigation of MASS should adhere to it. However, the applicability of MASS to COLREGS has not yet been resolved. Responding to these issues, the paper proceeds to analyze the principal challenges of MASS to COLREGS in terms of the application of MASS to good seamanship, the neglect provision, the lookout provision, the insight of one another provision, and the problems of the deviation provision. Furthermore, suggestions are put forth for the revision of COLREGS, including the mode of revision, the reconstruction of MASS collision liability, the long-term coexistence of MASS and traditional ships, and the risk control of COLREGS revision in the context of artificial intelligence. These suggestions aim to establish a foundation for a more effective adaptation to the advent of the MASS era.

Safety Assurance of Maritime Autonomous Surface Ships

Abstract Safety of Maritime Autonomous Surface Ships (MASS) is inherently complex owing to the dynamic and unpredictable nature of the maritime environment. This is exacerbated by the pace of change in enabling technologies and their disruptive impact. Integral to the safety assurance of MASS is assessment and mitigation of risk. The International Maritime Organisation (IMO) defines Risk as “the combination of frequency and severity of the consequence”. The advent of software-controlled systems has introduced difficulties in quantifying frequencies; an issue which is more prevalent in the context of autonomy due to the complex interaction between control algorithms and the variabilities of the environment in which they are deployed. Within complex autonomous systems, therefore it is challenging to perform quantitative risk assessments using failure rates, which gives rise to a need for different techniques to assess risk. Novel risk assessment methodologies such as those presented in the European Maritime Safety Agency (EMSA) Risk Based Assessment Tool (RBAT) combined with conventional risk assessment techniques can offer a solution to this challenge. Compliance with legislation and standards is another cornerstone of safety assurance, which is also inherently difficult for MASS vessels owing to their having been developed for conventional vessels. A goal-based approach to legislation compliance could present an interim solution until the IMO goal-based Maritime Autonomous Systems (MAS) provides harmonisation across the industry. Whilst MASS Safety assurance is non-trivial, there are solutions to many of the challenges faced; this paper examines the predominant challenges and presents potential solutions.

Consideration of Simulation Scenarios for Safety Assessment of Heading, Speed and Track Control Function

Abstract MASS (Maritime Autonomous Surface Ship) has been under active development in recent years. Especially, a great deal of attention has been paid to the development of the system which automates the navigation task of a ship including situation awareness function, collision and grounding avoidance function and heading, speed and track control function. When considering a MASS for coastal or open sea voyages, verifying the heading, speed and track control function presents a significant challenge during harbour manoeuvring, because of including automatic berthing and unberthing. Thus, it is essential to establish a verification method through simulation technique. While numerous studies have been conducted to develop control algorithms, few have focused on safety assessment, especially in the creation of simulation scenarios. Therefore, this study proposes a method of creating simulation scenarios to assess the safety of heading, speed and track control function for MASS in harbour manoeuvring. There are two main issues. The first is how to set up the passage plan to be included in the scenario, and the second is how to provide environment conditions. To solve the initial issue, a classification of navigational modes is defined, and the possible manoeuvring patterns that can be performed in each mode are organised. To solve the second issue, the method of assigning wind speed and direction to the scenario is examined based on regulations and observational data. Finally, an illustrative scenario is presented to demonstrate the proposed method.

Gap analysis and harmonization of International Standards for Maritime Autonomous Surface Ships

Abstract This paper addresses the challenges and solutions associated with the implementation of autonomous ships, focusing on the Maritime Autonomous Surface Ships (MASS) Code developed by the International Maritime Organization (IMO). With the goal of adopting a mandatory goal-based MASS Code by 2030, this study examines the interoperability and standardization requirements necessary for the effective global operation of autonomous ships. The paper analyses the MASS Code in relation to existing international standards, identifies gaps, and proposes standards works to accommodate the emerging technologies. Additionally, it investigate on the application of Autonomous Navigation Systems (ANS) and introduces the concepts of Shore ANS and Shore Bridge to enhance remote control capabilities. The analysis emphasizes the need for comprehensive standardization to ensure the safe and efficient operation of autonomous ships in diverse maritime environments.

Scenarios of Multiple Vessels Encounter for Safety Evaluation for Automatic Collision Avoidance Algorithm using AIS Data

Abstract Interest in Maritime Autonomous Surface Ships (MASS) is growing, and efforts are underway in Japan to realize them. One of the key issues in realizing autonomous operation is automatic collision avoidance. Simulation is effective in developing and evaluating such algorithms. This paper describes the generation of a set of simulation scenarios that consider the actual sea traffic using AIS data, which is traffic flow data in real seas. We assumed that the comprehensiveness of the scenarios could be ensured by generating scenarios by combining typical encounter patterns and collecting difficult situations that are encountered in real situations. First, the characteristics of the target sea area were identified from the AIS data, then difficult traffic situations were extracted from the data, and then a set of scenarios were generated based on them. Considering the presence of manned ships in the test area, we conducted some tests of the generated scenarios not only using fast-time simulations but also using a ship-handling simulator and conducted visual checks from own ship and the target ship.

Preface

These proceedings contain the peer-reviewed full papers presented at the joint conference ICMASS (the 6th International Conference on Maritime Autonomous Surface Ships) and MTEC (International Maritime Port Technology and Development Conference), held October 29th – 30th, 2024, in Trondheim, Norway. MTEC and ICMASS bring together academia and industry from around the world showcasing cutting-edge research and development within maritime technology, autonomous ships and port operations. https://www.ntnu.edu/mtec-icmass2024/home Trondheim, Norway’s hub for technological innovation, has utilized its rich maritime heritage to position itself at the forefront of maritime innovation. With the world’s first test area for autonomous ships, its strong position within academia and research, Trondheim has gained a leading position in the field of maritime innovation through collaboration between maritime stakeholders. This year’s conference topics include: • Green shipping and ports • Smart and autonomous ship and port operations • Maritime safety • Maritime digitalization, connectivity and cybersecurity The theme of this year’s conference, featuring contributions covering topics such as AI & autonomy, cybersecurity, onboard control, remote & shore control, smart ships as part of smart logistics, green shipping, safety evaluations, collision avoidance, regulatory & legal frameworks, as well as discussions on liability, etc. The papers, published in these proceedings contain theoretical contributions as well as reports on practical implementations in field labs and industry, and the conference will also have oral only presentation. Totally there will be more than 90 presentations, where more than 60 papers are invited. A conference such as this can only succeed as a team effort, and, as such, we thank the International Program Committee and the Reviewers for their excellent work in reviewing the papers as well as their invaluable input and advice. On behalf of the organizing committee, List of MTEC/ICMASS-24 Organizing Committee, International Programme and Management Committee and International Reviewers are available in this Pdf.

Perform assessment of COLREGs onboard a maritime autonomous surface ship: Narrow Channels and Traffic Separation Schemes

Abstract The usage of Maritime Autonomous Surface Ships in coastal and heavily used maritime environments necessitates advanced navigation systems capable of fully complying with the International Regulations for Preventing Collisions at Sea 1972 and not only open sea encounters. A significant challenge in this domain is enabling autonomous vessels to understand and navigate complex collision avoidance scenarios dictated by Rule 9 Narrow Channels and Rule 10 Traffic Separation Schemes. These rules are not well covered by research and only partial solutions have been proposed. To address this, we propose a ruleset for a system that can blend the traffic situation and geometric constraints extracted from Electronic Navigational Charts in S-57 format. The proposed ruleset extends our previous work on operationalization of COLREGs by introducing spatial features describing the maritime geometries relevant for traffic situations in Narrow Channels and Traffic Separation Schemes. We apply the ruleset to various traffic situations, showing how the inclusion of geometric context improves decision-making by reducing ambiguity and therefore enables the assessment of traffic situations and ship encounters in the vicinity of Narrow Channels and Traffic Separation Schemes. Our work outlines how the integration of spatial features into a rule-based system enables autonomous navigation capabilities of MASS for safe operations and compliance with maritime regulations.

Risk Analysis on Autonomous Vessels based on Systems Theory – Application of NET-HARMS method

Abstract The shift towards Maritime Autonomous Surface Ships is a significant development in the maritime logistics industry, with the potential to enhance efficiency, safety, and environmental sustainability. However, the integration of autonomous systems also presents new challenges and risks, particularly in the absence of empirical data for traditional risk assessment methodologies. This research tackles this problem by utilizing the Net-worked Hazard Analysis and Risk Management System (Net-HARMS) method, a systems thinking method that hasn’t been previously employed in examining MASS. The method analyses the risks associated with the EC-funded, H2020, MOSES Project, which included a concept for automating the manoeuvring and docking processes with autonomous tugboats. The Net-HARMS method offers a comprehensive and holistic approach to risk assessment, overcoming the limitations of conventional probabilistic models. By constructing a Hierarchical Task Analysis and a task network, the research maps the system’s operational framework and explores task interdependencies. The use of a risk mode taxonomy allows for the identification of task-specific and emergent risks, which are then assessed by utilising the risk matrix of the Risk-Based Assessment Tool developed by DNV, to assess the final risk as a function of the effectiveness of each risk mitigation layer and the severity of the identified task consequences. The findings provide valuable insights into critical tasks requiring enhanced risk control measures and contribute to the development of safety constraints necessary for the successful implementation of autonomous shipping technologies. By applying Net-HARMS method to the realm of autonomous ships, this research not only fills a significant gap in maritime risk analysis but also sets a precedent for future studies in this rapidly evolving field.

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Transformative Impact of the EU AI Act on Maritime Autonomous Surface Ships

The International Maritime Organization collaborates with member states to develop the Maritime Autonomous Surface Ships (MASS) Code to establish an international agreement framework. In December 2023, an agreement was reached on the European Union (EU) Artificial Intelligence (AI) Act within the MASS Code. It was officially finalized and implemented on 1 August 2024. While the Act’s full application is set for two years later, certain provisions regarding safety against AI risks will take effect sooner. This study utilized a multiple-streams analysis method to examine how the EU AI Act impacted shipyards and shipping companies, focusing on ethical AI use and user safety. The findings indicate that technical completeness, safety enhancement, and AI system regulation through risk classification influence autonomous ship innovation. The challenges of reviewing the commercialization of new technologies for autonomous ships may result in market failures or decreased industry competitiveness. The study emphasizes the need to balance AI safety and ethical responsibility with autonomous ship development innovations. To ensure the sustainable application of AI technology, innovators must adapt to the EU AI Directive. Hence, this study confirms that the ethical and legal regulation of AI technology is crucial in technological innovation and development related to autonomous ships.