Distribution-driven generation model of collision risk scenarios for MASS collision avoidance system verification

Collision Avoidance Systems for Maritime Autonomous Surface Ships Considering Uncertainty in Ship Dynamics

A ship collision avoidance system for human-machine cooperation during collision avoidance

This chapter examines the legal standing of Maritime Autonomous Surface Ships (MASS) in relation to international conventions which govern the use of commercial vessels and discusses their potential contractual and non-contractual realities. MASS routinely operate with little or no human involvement and have some or all of the following characteristics. The level of autonomy of a MASS depends on its function. Existing MASS display a number of varying degrees of autonomy and can be subdivided into different classes. MASS falling into the category of periodically unmanned ship would be able to operate without a crew on board for extended periods of time. For MASS to operate effectively they will require collision avoidance systems that are compliant with the collision regulations. The conventions may become applicable in the future when the construction of larger MASS is financed by providing security over the MASS through a mortgage.

With the advent of the Fourth Industrial Revolution, technologies based on Information and Communication Technology, the Internet of Things, and Artificial Intelligence are rapidly advancing. Maritime Autonomous Surface Ships (MASS) represent a key area in this transformation. The International Maritime Organization (IMO) is developing international regulations to ensure safe MASS operation. A non-mandatory framework is expected by 2026, with mandatory regulations entering into force in 2032. Accordingly, not only the Republic of Korea but also other major shipping nations should prepare to incorporate these regulations into national law. This study examines the global trend toward MASS adoption and the need to restructure Korea’s domestic legal and institutional frameworks. It assesses the compatibility and adequacy of Korea’s current legal instruments. The findings show that the Act on the Promotion of Development and Commercialization of MASS mainly targets technology development and pilot operation and verification, and thus fails to address several regulatory elements required by the MASS Code. In response, this paper proposes a phased legislative strategy to improve Korea’s regulatory readiness and establish a solid institutional foundation for the safe, internationally recognized commercialization of MASS.

Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey

Maritime autonomous surface ships (MASS), presenting the future of maritime transport, are attracting increasing attention from the international maritime community. The collision risk analysis of MASS reveals unsolved challenges, which without appropriate solutions, will result in the error prone development of the relevant risk control measures and policies. Among the challenges, two significant ones in the existing literature are the lack of historical failure data to realise quantitative risk assessment, and 2) the complex causal relationship among the relevant risk factors. This paper aims to develop a new Fault Tree Analysis-Fuzzy Bayesian Network (FTA-FBN) model to conduct the collision risk assessment of MASS with uncertainty in data. First, it establishes a causal relationship among the risk factors through an FTA. Secondly, mapping the obtained FTA diagram into a BN allows fault diagnosis and the identification of the most important factors influencing MASS collisions. In this process, a survey is conducted to collect the primary data for configuring the conditional probabilities of the relevant influential factors and quantifying the developed BN for risk diagnosis and prediction. Finally, the new model is verified by using sensitivity analysis and three axioms and then applied to conduct scenario-based risk prediction and diagnosis to generate insightful findings to guide MASS navigation safety. The results demonstrate that the FTA-FBN model realizes the simplification of the expert scoring process, reduces computational complexity, and addresses the challenge of constructing causal relationships between MASS collisions and their risk factors due to the scarcity of historical accident data. Additionally, the BN backward reasoning identifies key collision risks, including external physical attacks, inadequate training of shore-based operators, insufficient maintenance of ship equipment and systems, and cyber-security threats. The new model when being adapted, can provide a reference for the formulation of safe navigation policies and provide important insights for shipping companies to ensure the safe navigation of their ships and shipbuilders to optimise ship design.

The reliability of collision avoidance systems for Maritime Autonomous Surface Ships is one of the most critical factors for their safety. In particular, since many ship collisions occur in coastal areas, it is crucial to ensure the reliability of collision avoidance algorithms in geographically limited coastal waters. However, studies on maritime autonomous surface ships collision avoidance algorithms mainly focus on the traffic factor despite the importance of the geographic factor. Therefore, this study presents a methodology for establishing a practical collision avoidance system test bed, considering the geographic environment. The proposed methodology is a data-driven approach that objectively categorizes collision risk situations by extracting these risks using Automatic Identification System (AIS) and Electronic Navigational Chart (ENC) data, followed by clustering algorithms. Consequently, the research results present a direction for establishing test beds from the perspective of geographic and traffic factors.

Maritime Autonomous Surface Ships (MASS) are quickly emerging as a game-changing technology in various parts of the world. They can be used for a wide range of applications, including cargo transportation, oceanographic research and military operations. One of the main challenges associated with MASS is the need to build trust and confidence in the systems among end-users. While the use of AI and algorithms can lead to more efficient and effective decision-making, humans are often reticent to rely on systems that they do not fully understand. The lack of transparency and interpretability makes it very difficult for the human operator to know when an intervention is appropriate. This is why it is crucial that the decision-making process of MASS is transparent and easily interpretable for human operators and supervisors. In the emerging field of eXplainable AI (XAI), various techniques are developed and designed to help explain the predictions and decisions made by the AI system. How useful these techniques are in a real-world MASS operation is, however, currently an open question. This calls for research with a holistic approach that takes into account not only the technical aspects of MASS, but also the human factors that are involved in their operation. To address this challenge, this study employs a simulator-based approach were navigators test a mock-up system in a full mission navigation simulator. Enhanced decision support was presented on an Electronic Chart Display & Information System (ECDIS) together with information of the approaching ships as AIS (Automatic Identification System) symbols. The decision support provided by the system was a suggested sailing route with waypoints to either make a manoeuvre to avoid collision, or to maintain course and speed according to the Convention of the International Regulations for Preventing Collisions at Sea (COLREG). After completing the scenarios, the navigators were asked about the system's trustworthiness and interpretability. Further, we explored the needs for transparency and explainability. In addition, the navigators gave suggestions on how to improve the decision support based on the mentioned traits. The findings from the assessment can be used to develop a strategic plan for AI decision transparency. Such a plan would help building trust in MASS systems and improve human-machine collaboration in the maritime industry.

The Collision Avoidance (CA) system constitutes a key enabling technology for the Maritime Autonomous Surface Ships (MASS), the appropriate functionality of which is critical for assuring the navigation safety. Although several techniques including testing of the collision scenarios in a virtual environment can be employed, the trust of testing phase results depends on the number of tested scenarios and their coverage. This study aims at proposing a systematic and automatic process for the generation of the traffic scenarios that can be employed for the CA system testing. First, the range of the investigated parameters is defined, and samples of potential traffic parameters are generated using Sobol sequences. Subsequently, hazardous traffic scenarios are identified from the initially generated scenarios by using predefined rules. For these hazardous scenarios, a risk vector considering weather conditions and traffic conditions is calculated. A clustering algorithm is employed to identify the groups of traffic conditions that can be encountered based on each scenario risk vector and COLREGs traffic scenarios. For each of these groups, the riskiest scenario is provided as input for the test cases development, thus, simplifying the selection process of testing scenarios. The process is applied to a theoretical Short Sea Shipping autonomous vessel, whereas the derived results are employed to discuss the advantages and disadvantages of the developed process.

The transportation systems are heading towards increasing autonomy in all domains, and the maritime field makes no exception. The International Maritime Organization has been working on releasing a regulatory framework for Maritime Autonomous Surface Ships (MASS) to keep pace with the technological developments in the field. Autonomous shipping forces the researcher and the designers to face a wide range of scientific challenges, such as navigation decision support systems, collision avoidance algorithms, path planning, navigation and control, sensor data processing and fusion, remote control, and communication, with the final intent of achieving a fully integrated and autonomous worldwide maritime transportation system, where a human-less collaborative conflict resolution could potentially retire the COLREGs. However, the maritime sector will first face a transition period where traditional ships will share the seas and interact with heterogeneous MASS with various autonomous capabilities featuring different and probably incompatible communication protocols. In such a scenario, the COLREGs will still play a primary role in helping the collision avoidance systems resolve conflicts and limiting the degrees of freedom. This paper aims to study the interaction among multiple vessels with autonomous collision avoidance capabilities operating in a close navigation scenario. The ships operate according to a COLREG-compliant collision avoidance algorithm. The paper relies on numerical simulation to systematically investigate different scenarios in which autonomous vessels operate and interact in the presence of fixed obstacles. Results are presented and critically discussed.

The introduction of maritime autonomous surface ships (MASSs) has prompted a significant shift in the role of navigators from traditional navigation to supervising artificial intelligence (AI) collision avoidance systems or managing operations from remote operation centers (ROCs). This may be problematic because the integration of AI technologies into collision avoidance systems may jeopardize safety if done in a way that reduces human control or leaves humans out of the loop. For onboard navigators or ROC operators who work with AI, it is important that the AI’s “thinking” and decisions are transparent and that alternative decisions are easy to execute. Regarding navigation and collision avoidance, this issue can be defined as AI decision transparency. In this systematic review, we examined state-of-the-art research on traffic alerts and collision avoidance systems with respect to decision transparency for autonomous shipping. Through a thematic analysis, we identified three main groups of transparency in the reviewed literature: strategies, visualization, and technology, with respective subgroups.

This research develops a performance evaluation model of humans in avoiding ship collision situations compared to the AI ship collision avoidance system (CAS). A human, the remote operator (RO) of Maritime Autonomous Surface Ships (MASS), ought to make compact collision avoidance (CA) decisions to secure safety and efficiency during remote operations. Hence, evaluating RO’s CA performance is important; however, research on developing evaluation methods concentrates merely on evaluating trainees based on instructor’s guidelines, while artificial intelligence (AI) makes CA decisions through parameter-based calculation. Therefore, this research proposes a CA performance evaluation model for RO of MASS based on CAS intervening of RO trainee’s simulation training in ship collision avoidance. In each time interval, the RO’s decisions showed divergent behaviors under given situational conditions compared to the CAS’s behaviors in equivalent situations. Findings denote the benefits of CAS intervention methods and RO performance evaluation model based on the proposed performance features.

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

With the development of emerging techniques, maritime autonomous surface ships (MASS) have attracted much attention, and the remote control ships’ future seems promising. However, due to communication issues, ship–shore transmission faces the challenge of time delay. The use of the transmitted information without compensation could reduce the effectiveness of controlling or could cause the remote control to be unstable. To eliminate the negative effects of uncertain delays during navigation, an Augmented State Cubature Kalman Filter (AS-CKF) is proposed. First, the uncertainty of the transmission delays is modeled using a probability density function (PDF). Second, the ship’s states are updated and estimated using the delayed observed data, and then the real state of the ship is simultaneously corrected in the augmented state vector. In this way, the delay compensation problem becomes a one-step prediction problem. To test the proposed AS-CKF for MASS, we simulate scenarios with the remote control ship under different communication time delays. The results show improvements compared to the traditional CKF, EKF, or AS-EKF, which indicates the potential of the proposed methods in remote control MASS.

The collision avoidance system is one of the core systems of MASS (Maritime Autonomous Surface Ships). The collision avoidance system was validated using scenario-based experiments. However, the scenarios for the validation were designed based on COLREG (International Regulations for Preventing Collisions at Sea) or are arbitrary. Therefore, the purpose of this study is to identify and systematize objective navigation situation scenarios for the validation of autonomous ship collision avoidance algorithms. A data-driven approach was applied to collect 12-month Automatic Identification System data in the west sea of Korea, to extract the ship’s trajectory, and to hierarchically cluster the data according to navigation situations. Consequently, we obtained the hierarchy of navigation situations and the frequency of each navigation situation for ships that sailed the west coast of Korea during one year. The results are expected to be applied to develop a collision avoidance test environment for MASS.