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Dynamic adaptive autonomous navigation decision-making method in traffic separation scheme waters: A case study for Chengshanjiao waters

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... This relationship likely corresponds to the cyclical rise and fall of temperatures across seasons, with SST peaking in summer and reaching lower levels in winter. Additionally, the moderate positive correlation between SST and SSH (0.42) may reflect a trend of rising sea surface height as temperatures increase, which could be partially attributed to thermal expansion effects [37]. When water temperature rises, ocean water volume expands, leading to an increase in sea surface height. ...
... Longitude-related variations may reflect the impact of different regional ocean circulation patterns, while the influence of Month suggests the regulatory role of seasonal factors, such as monsoons, precipitation, and evaporation, on salinity. The positive correlation between SSH and SST (0.42) may be associated with the thermal expansion caused by rising sea surface temperatures [37]. When SST increases, rising water temperatures reduce density, causing the water volume to expand and increasing the sea surface height. ...
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To evaluate and compare the effectiveness of prediction models for Argentine squid Illex argentinus trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop Illex argentinus trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., Illex argentinus). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas.
... Other recent path planning methods for ships include a virtual potential field (VPF)based method [6,7] and a method based on dynamic time-space network trees for solving encounters with multiple ships [8]. In [9], the authors introduced a dynamic adaptive decision-making method for application in traffic separation scheme (TSS) waters. The researchers in [10] present a survey of recent path planning methods for Maritime Autonomous Surface Ships (MASS). ...
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The autonomous operation of a device or a system is one of the many vital tasks that need to be achieved in many areas of industry. This is also true for maritime transport. This paper introduces an approach developed in order to achieve the autonomous operation of a ship in a port. A safe trajectory was calculated with the use of the Ant Colony Optimization (ACO) algorithm. The ship motion control was based on two controllers: the master overriding trajectory controller (OTC) and the slave low speed controller based on the Linear Matrix Inequalities (LMI) method. The control object was the model of a Very Large Crude Carrier Blue Lady. The results of our simulation tests, which show the safe trajectories calculated by the ACO algorithm and executed by the ship using the designed controllers (OTC and LMI), are presented in this paper. The results present maneuvers executed by the Blue Lady ship when at port. The area where the tests were conducted is located in the Ship Handling, Research and Training Center of the Foundation for Shipping Safety and Environmental Protection on the Lake Silm in Kamionka, Poland.
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In the last few years, autonomous ships have attracted increasing attention in the maritime industry. Autonomous ships with an autonomous collision avoidance capability are the development trend for future ships. In this study, a ship manoeuvring process deduction-based dynamic adaptive autonomous collision avoidance decision support method for autonomous ships is presented. Firstly, the dynamic motion parameters of the own ship relative to the target ship are calculated by using the dynamic mathematical model. Then the fuzzy set theory is adopted to construct collision risk models, which combine the spatial collision risk index (SCRI) and time collision risk index (TCRI) in different encountered situations. After that, the ship movement model and fuzzy adaptive PID method are used to derive the ships’ manoeuvre motion process. On this basis, the feasible avoidance range and the optimal steering angle for ship collision avoidance are calculated by deducting the manoeuvring process and the modified velocity obstacle (VO) method. Moreover, to address the issue of resuming sailing after the ship collision avoidance is completed, the Line of Sight (LOS) guidance system is adopted to resume normal navigation for the own ship in this study. Finally, the dynamic adaptive autonomous collision avoidance model is developed by combining the ship movement model, the fuzzy adaptive PID control model, the modified VO method and the resume-sailing model. The results of the simulation show that the proposed methodology can effectively avoid collisions between the own ship and the moving TSs for situations involving two or multiple ships, and the own ship can resume its original route after collision avoidance is completed. Additionally, it is also proved that this method can be applied to complex situations with various encountered ships, and it exhibits excellent adaptability and effectiveness when encountering multiple objects and complex situations.
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Collisions at sea are frequently caused by human-related factors, such as; manoeuvre timing mistakes, risk assessment failures and deficiencies in strategies for collision avoidance. These factors reveal the importance of the automation systems in providing safety of navigation. Thus, a decision support system was developed in this study that can be a reference to the ship operators in the implementation of the collision avoidance action, in case of an encounter situation involving risk of collision. Both qualitative and quantitative methods were conducted in the study. In the qualitative research process, the variable constraints in the mathematical model and the inputs of the scenarios implemented in experiments were determined based on the findings obtained from experts’ interviews. In the quantitative research process, the problem-solution was reached with the developed algorithm (ColAv_GA), which is formed based on the Genetic Algorithm and Fuzzy Logic. The developed algorithm was validated in a virtual environment using a bridge simulator, and in a real environment with an autonomous surface vehicle (ASV), with satisfactory results. The output of this research is expected to contribute to the safety of navigation. The developed algorithm can be used as a collision avoidance sub-module for autonomous ships and ASV.
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COLREGs-based collision risk awareness model is urgently needed in real-time operating conditions. However, this is a complicated problem under various encounter situations, some of which are very complex. In order to quantify the collision risk in real operating conditions, a novel risk-informed collision risk awareness approach is proposed for real-time operating conditions. Firstly, the ship's actions are identified based on the Automatic Identification System (AIS) data. Secondly, the uncertainty of ship action patterns is analyzed by regarding the target ships as velocity obstacles. Then, the collision risk model is utilized to assess the collision risk level based on the uncertainty in the non-linear velocity obstacles algorithm considering responsibility. Finally, some case studies are carried out based on the proposed model. In the model, the dynamic and uncertainty features of the ship action dynamics in real operating conditions are considered, which could benefit on reducing ship collision accidents and improving the development of technologies on intelligent collision avoidance decision makings.
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Predicting the likelihood of maritime accidents is hindered by the relative sparsity of collisions on which to develop risk models. Therefore, significant research has investigated the capability of non- accident situations, near misses and encounters between vessels as a surrogate indicator of collision risk. Whilst many studies have developed ship domain concepts, few have considered the practical considerations of implementing this method to characterise navigational risk between waterways and scenarios. In order to address this, within this paper we implement and evaluate the capability and validity of domain analysis to characterise and predict the likelihood of ship collisions. Our results suggest that the strength of the relationship between collisions and encounters is varied both between vessel types and the spatial scale of assessment. In addition, we demonstrate some key practical considerations in utilising domain analysis to predict the change in collision risk, through a hypothetical wind farm. The outcomes of this study provide research direction for practical applications of domain analysis on collision risk assessments.
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Small ship detection is an important topic in autonomous ship technology and plays an essential role in shipping safety. Since traditional object detection techniques based on the shipborne radar are not qualified for the task of near and small ship detection, deep learning-based image recognition methods based on video surveillance systems can be naturally utilized on autonomous vessels to effectively detect near and small ships. However, a limited number of real-world samples of small ships may fail to train a learning method that can accurately detect small ships in most cases. To address this, a novel hybrid deep learning method that combines a modified Generative Adversarial Network (GAN) and a Convolutional Neural Network (CNN)-based detection approach is proposed for small ship detection. Specifically, a Gaussian Mixture Wasserstein GAN with Gradient Penalty is utilized to first directly generate sufficient informative artificial samples of small ships based on the zero-sum game between a generator and a discriminator, and then an improved CNN-based real-time detection method is trained on both the original and the generated data for accurate small ship detection. Experimental results show that the proposed deep learning method (a) is competent to generate sufficient informative small ship samples and (b) can obtain significantly improved and robust results of small ship detection. The results also indicate that the proposed method can be effectively applied to ensuring autonomous ship safety.
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Ship domain is crucial for traffic risk assessment and safe navigation at sea especially in high-density traffic. This study proposes a fuzzy rule-based model to create an asymmetrical polygonal ship domain for both restricted waters and open waters. The model takes into consideration various navigational factors which are determined as a result of expert interviews. A C# application based on fuzzy inference system (FIS) with Mamdani model has been implemented to create the size and shape of the domain. Finally, a few experimental tests and comparative implementations with other ship domains have been conducted to demonstrate the performance of the proposed system. The proposed ship domain will undoubtedly contribute to the ship traffic engineering and is considerably utilized in collision risk assessment and traffic design.
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Maritime collision risk prediction is crucial for the safety management of ocean transportation. Previous studies have primarily focused on near-miss collision risk of ship pairs, yet the risk due to congestion caused by multiple ships is significant. This paper proposes a novel space-time geographical approach for addressing multi-ship near-miss collision risk based on vessel motion behavior. The direction-constrained space-time prism is used to characterize the interaction possibility of ships, enabling potential collision risk to be evaluated. The advantage of direction-constrained space-time prism in analyzing ship movements is that it accounts for direction limitation thereby eliminating unreasonable estimation associated with the assumption of arbitrary changes in sailing direction through the classical space-time prism. This paper uses the trajectories of ships traveling the southeast coast of China to investigate the viability of the proposed approach. In comparison to the fuzzy quaternion ship domain model and closest point of approach-based methods, the proposed approach is capable of identifying hierarchical near-miss collision risks for different ships to improve risk evaluation. This is essential for ship path optimization that accounts for changes in the speed and course of ships. This work supports maritime collision risk forecasting, but also provides detailed insights for actionable steps to reduce risks.
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In this paper, a deep reinforcement learning (DRL)-based controller for path following of an unmanned surface vehicle (USV) is proposed. The proposed controller can self-develop a vehicle’s path following capability by interacting with the nearby environment. A deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic-based reinforcement learning algorithm, was adapted to capture the USV’s experience during the path-following trials. A Markov decision process model, which includes the state, action, and reward formulation, specially designed for the USV path-following problem is suggested. The control policy was trained with repeated trials of path-following simulation. The proposed method’s path-following and self-learning capabilities were validated through USV simulation and a free-running test of the full-scale USV.
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To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.
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As the number of ships for marine transportation increases with the globalisation of the world economy, waterways are becoming more congested than before. This situation will raise the risk of collision of the ships; hence, an automatic collision avoidance system needs to be developed. In this paper, a novel approach based on deep reinforcement learning (DRL) is proposed for automatic collision avoidance of multiple ships particularly in restricted waters. A training method and algorithms for collision avoidance of ships, incorporating ship manoeuvrability, human experience and navigation rules, are presented in detail. The proposed approach is investigated not only by numerical simulations but also by model experiments using three self-propelled ships. Through the systematic numerical and experimental validation, it is demonstrated the developed approach based on the DRL has great possibility for realising automatic collision avoidance of ships in highly complicated navigational situations.
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This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.
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Maritime accidents have been imposing various risks to individuals and societies in terms of human and property loss, and environmental consequences. For probabilistic risk analysis and management, collision candidate detection is the first step. Therefore, it is of great importance to further improve methods to detect possible collision scenarios. This paper proposes a Time Discrete Non-linear Velocity Obstacle (TD-NLVO) method for collision candidate detection, which is based on the Non-linear Velocity Obstacle algorithm and tested on historical AIS data (Automatic Identification System). Collision candidates are detected based on the perspective which considers a ship encounter as a process, rather than analysing traffic data at certain time slices. Case studies on single encounters of ship traffic in waterways environments are conducted and presented in this paper. The results indicate that the TD-NLVO method can effectively detect collision candidates which satisfy pre-set criteria. A comparison between seven other popular AIS data-based collision candidate methods is performed, and the results indicate that the proposed method outperforms the other methods regarding its robustness towards the choice of parameter settings.
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It is important to establish all the causes of marine accidents, but this is sometimes quite difficult. Therefore, analyzing the causes by examining as many accidents as possible using a common classification system and submission of proposals is extremely essential. Overall, the study is an examination of collision and grounding accidents using the Human Factors Analysis and Classification System (HFACS). In the first phase of the study, the frequency and distribution of the causes of collision and grounding accidents were examined by HFACS categories. In the second phase, unsafe acts, which have been identified as the most important categories, and preconditions for unsafe acts are evaluated by bridge crew structure. The Chi-Square Test of Compliance and Independence and Simple Correspondence Analysis are used as statistical methods. As a result of study, the most important causes are identified as human factor differences between collision and grounding accidents, decision errors, resource management deficiencies, violations, skill-based errors and miscommunication.
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Ship collision avoidance is highly dependent upon seamanship and rules. When ship collision risk exists, proper collision avoidance actions must be taken according to the correct encounter situation and determined stage. All autonomous collision avoidance (ACA) operations in the future must comply with given rules and seamanship practices, which make the quantitative analysis of them prerequisites for ACA. This study presents a novel quantitative analysis system for the International Regulations for Preventing Collisions at Sea (COLREG) Rules and Seamanship. The proposed system consists of three parts: an encounter situation discrimination model based on the mutually relative bearing of the “target ship” (TS) and “own ship” (OS); a stage discrimination model representing the extent of collision risk per different domain models for every potential situation and a model to determine collision avoidance action per COLREG, seamanship, and ship maneuverability information was established accordingly. The collision avoidance plans appropriate for different situations and stages are generated based on the rules, seamanship, and rudder steering direction judgments. A simulation scenario was utilized to validate the effectiveness and feasibility of the system.
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This article proposes a modified cognitive reliability and error analysis method (CREAM) for estimating the human error probability in the maritime accident process on the basis of an evidential reasoning approach. This modified CREAM is developed to precisely quantify the linguistic variables of the common performance conditions and to overcome the problem of ignoring the uncertainty caused by incomplete information in the existing CREAM models. Moreover, this article views maritime accident development from the sequential perspective, where a scenario- and barrier-based framework is proposed to describe the maritime accident process. This evidential reasoning-based CREAM approach together with the proposed accident development framework are applied to human reliability analysis of a ship capsizing accident. It will facilitate subjective human reliability analysis in different engineering systems where uncertainty exists in practice.
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A ship domain may be thought of as the sea around his ship which the navigator would like to keep free, with respect to other ships and fixed objects. In this paper, which was read at an Institute meeting in London on 12 February 1975 with Captain R. May-bourn in the Chair, Mrs. Goodwin describes studies to determine the dimensions of a domain based on radar simulator performance and traffic surveys in the North Sea. She also suggests applications of this domain concept to marine traffic engineering problems and to traffic control schemes. © 1975, The Royal Institute of Navigation. All rights reserved.
Article
Marine collision accidents cause a great loss of lives and property. As a possible solution, the danger immune algorithm is used to achieve ship collision avoidance strategy optimization, which is a multi-objective problem concerning safety and economy. Collision avoidance operations are encoded as the individuals of optimization algorithm. In the system, ship domain and ship arena, among others, are used for collision risk evaluation to assess the fitness of individuals. Through the optimization, the navigator will obtain the optimal collision avoidance strategy to achieve safe and efficient collision avoidance. The simulations indicate that the optimization algorithm is valid.
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Traffic capacity is the capability of a waterway to deal with the traffic and when the traffic volume exceeds this limit traffic flow stops, as is often experienced on congested roads. According to the U.S. Government Highway Capacity Manual the capacity of a highway with four or more lanes and free from conditions hindering smooth traffic flow has a maximum value of V / Y min per lane, where V is the speed of a group of vehicles and Y min is the average minimum separation of cars. Since Y min is a function of V and increases rapidly and continuously with speed, there is a maximum value for V / Y min which is a function of the speed. This is called basic capacity. Several examples of capacity together with the speeds of the transportation facilities are given in Table I.
Article
This paper outlines the concept of a domain and an evasion area, called an arena, around a ship which are then applied to produce a computer model of ship behaviour. The arena determines when a ship takes avoiding action, as does the land arena which reacts with a discrete series of coastal points to prevent the ship running aground. The increase in the number and size of ships has resulted in the introduction of traffic routing schemes and the need to understand ship behaviour more thoroughly. The concept of ‘the effective area around a ship which a navigator would like to keep clear with respect to other ships and stationary objects’ has been used by various authors including Goodwin, Fujii and Lewison with varying names such as domain, collision diameters and encounter area. There has been no fixed shape for these areas. Some are circular, others elliptical, while Goodwin's has three segments each with its own portion of a circle. By developing the theory of the domain, it was hoped to be able to produce a model of traffic behaviour which could be used to simulate traffic flows, or specific incidents, in order to study them more fully.
Article
This paper focuses on a fuzzy logic based intelligent decision making system that aims to improve the safety of marine vessels by avoiding collision situations. It can be implemented in a decision support system of an oceangoing vessel or included in the process of autonomous ocean navigation. Although Autonomous Guidance and Navigation (AGN) is meant to be an important part of future ocean navigation due to the associated cost reduction and improved maritime safety, intelligent decision making capabilities should be an integrated part of the future AGN system in order to improve autonomous ocean navigational facilities. In this study, the collision avoidance of the Target vessel with respect to the vessel domain of the Own vessel has been analyzed and input, and output fuzzy membership functions have been derived. The if–then rule based decision making process and the integrated novel fuzzy inference system are formulated and implemented on the MATLAB software platform. Simulation results are presented regarding several critical collision conditions where the Target vessel fails to take appropriate actions, as the “Give way” vessel to avoid collision situations. In these situations, the Own vessel is able to take critical actions to avoid collisions, even when being the “Stand on” vessel. Furthermore, all decision rules are formulated in accordance with the International Maritime Organization Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), 1972, to avoid conflicts that might occur during ocean navigation. KeywordsAutonomous Guidance and Navigation–Collision avoidance–IMO rules and regulations–COLREGs–Fuzzy logic–Intelligent systems–Decision making process–Crash stopping
Intelligent collision avoidance decision-making method for unmanned ships based on driving practice
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