Collision between ships is one of the major contributors to ship accidents. To facilitate the development of the real-time collision risk analysis model for collision avoidance, in this research, an improved Rule-aware Time-varying Collision risk Model is proposed, which considers the estimation of target ship motion and the corresponding uncertainty in the risk analysis process. To make the collision risk model more in line with the actual situation, ship maneuverability, the COLREGs, and good seamanship are considered and integrated into the framework of the TCR model. Firstly, the Gaussian process is used to predict the potential trajectories of the target ship. Secondly, the Probabilistic Velocity Obstacle (PVO) model is utilized to integrate the uncertainty of the target ship's motion into the collision risk model. The collision risk is therefore formulated as a ratio of available maneuvers leading to a collision to all available maneuvers. To verify the effectiveness of collision risk, two actual target ships and six groups of collision risk detection experiments under different encounter scenarios were carried out. Compared with the traditional collision risk index model and original R-TCR model, the collision risk detected by the Improved R-TCR model is closer to the actual situation.@en

Trajectory planning is one of the important technologies to ensure the safe navigation of the unmanned ship. This paper presents a dynamic path planning method based on the multi-layer Morphin adaptive search tree algorithm, which considers ship maneuverability, COLREGS, and good seamanship to harmonize the actions in the mixed traffic environment. First, the environment model is built according to the environment information of the rolling window; second, the feasible avoidance range of collision avoidance is calculated according to the velocity obstacle (VO) method. Finally, path optimization is carried out using the Morphin adaptive search tree algorithm. Through a case study and comparison with traditional artificial potential field (APF) models, the applicability and potential of the method are verified. This model can be applied to the autonomous navigation for unmanned ships as well as conventional manned ships and demonstrate good potential in smart shipping.

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Collison between ships is one of the major contributors to maritime accidents. To reduce ship collision accidents, the research on collision avoidance decision-making has been drawing much attention from various parties. In this research, extensive literature and expert knowledge are collected and analyzed to identify the common sense and discrepancies between collision avoidance decision-making for theoretical research and navigation practices. The key factors that are considered in the two perspectives are identified and discussed, based on which, the knowledge structures that can represent the development of the process in the two perspectives are established. A series of comparisons between the knowledge structure based on theoretical research and navigation practices are conducted. The comparisons indicate clear common sense and discrepancies between the theoretical research and navigation practices regarding collision avoidance decision-making. The potential causes of them are also analyzed. The research results would be beneficial for the development of collision avoidance decision-making for both autonomous and conventional manned ships in maritime traffic.

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Autonomous navigation on the open sea involving automatic collision avoidance and route planning helps to ensure navigational safety. To judge whether all target ships (TSs) will pass safely and find the optimal route under multi-ship encounter situations, the relationship between the variations in the own ship (OS) velocity vector after nonlinear course altering motion and the collision avoidance result, which is defined as the collision avoidance mechanism, was analyzed. Methods producing the optimal route were also proposed. First, the static collision avoidance mechanism based on the ship domain and velocity obstacle (VO) was introduced. On that basis, the collision-free course alteration range of the OS, without consideration of the real manoeuvring process, was presented. Second, the ship motion equations and fuzzy adaptive proportion integral derivative (PID) control method were combined to develop a course control system. This system was then used to predict OS motions during the course-altering process. Based on this prediction, TS positions were calculated. Subsequently, the dynamic collision-free course altering ranges for the OS were obtained. Third, a model to compute the optimal route was introduced. Finally, simulations were performed under a situation including six TSs and two static objects, and the shortest collision-free route that satisfies both regulations for preventing collisions and good seamanship was found.

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Maritime transportation has been one of the major contributors to the global trade and economy. Accidents, however, have been continuously posing risks to individuals and societies in terms of loss of human life, economic and environmental consequences, etc. This thesis paid particular attention to the probabilistic risk analysis of ship collision accident and explored the possible influence of implementing MASS on the risk of collision in maritime traffic. A comprehensive literature review is conducted to investigate the stakeholders on maritime traffic safety and related methodologies for risk analysis of ship collision accident. A series of methods based on Non-linear Velocity Obstacle (NLVO) is proposed to identify the encounter between ships that have the potential for collision accident from the perspective of the whole encounter process. The causal relationships between accident contributing factors are modelled with credal network to estimate the causation probability of ship collision accident with consideration of encounter situation. Finally, an initial analysis of the potential influence of MASS on the collision risk in maritime traffic was also explored based on the proposed approaches. The objective of this research is to furtherly develop a quantitative risk analysis model for ship collision accident in waterways in an integrated manner that can introduce multiple sources of information into analysis and further to obtain insights of collision risk for safety management.@en

In this research, a hybrid approach for global path planning for Maritime Autonomous Surface Ship (MASS) is proposed, which generates the shortest path considering the collision risk and the proximity between path and obstacles. The collision risk concerning obstacles is obtained using Time-Varying Collision Risk (TCR) concept, taking into account the velocity constraint of the ship that can achieve during operation. The influence of proximity from obstacles is measured with the Fast Marching (FM) algorithm. A new cost function is proposed allowing to combine the influence of obstacle proximity and collision risk in the region. Finally, the Fast Marching Square algorithm is applied to generate the globally optimal path that can reach the pre-set destination. The contribution of this work is two-fold: 1) considering the velocity constraint of the own ship, together with its influences of collision risk into the global path planning stage of autonomous navigation. 2) measuring the collision risk induced by the obstacles from their comprehensive influences on the achievable velocity range using TCR concept, instead of numerical integration of risk measurement. The results of the case study indicate that the proposed approach can find an optimal path considering the collision risk and proximity from the obstacles.

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Collision prevention is critical for navigation safety at sea. At early ages, researchers aimed at developing navigational assistance systems for enhancing situational awareness of human operators as human is at the core of collision avoidance. Recently, autonomous vehicles have gained a remarkable amount of attention with a focus on solving collision problems by machines. This results in two groups of studies, both working on preventing collisions but with different focuses: one aims at conflict detection, and the other focuses on conflict resolution. This paper offers a comprehensive overview of collision prevention techniques based on the three basic processes of determining evasive solutions, namely, motion prediction, conflict detection, and conflict resolution. The strengths and weaknesses of different methods for these three fundamental processes are discussed. Limitations and new challenges are highlighted. Moreover, this review points out the differences between the research for manned and unmanned ships and how the research in the two domains can learn from each other. A potential roadmap for the transition from existing manned ships to fully unmanned ships is provided in the end.

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Ship collision is one of the major contributors of maritime accidents. Quantitative risk analysis of such accidents is an effective tool for maritime safety administrations to understand the current risk level and propose risk mitigation measures. In this paper, an improved Time Discretized Non-linear Velocity obstacle (TD-NLVO) algorithm is proposed to detect multiple ship encounter situations using historical AIS data. Boolean operation on the individual NLVO is integrated with TD-NLVO using the union of the velocity-obstacle sets to determine a dangerous encounter situation according to the pre-set criteria. Two case studies are implemented to illustrate the capability of the proposed algorithm. A comparison is conducted between the previous and the improved methods. The results indicate that the improved method can effectively identify a multiple ship encounter which satisfies the pre-set criteria. The improved method has the potential to provide more detailed information for stakeholders e.g. maritime safety administration, etc. to propose risk mitigation measures as well as to improve the accuracy of geometric probability analysis for ship collision risk.

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The maritime shipping industry has been making significant contributions to the development of the regional and global economy. However, maritime accidents and their severe consequences have been posing an incrementing risk to the individuals and societies. It is therefore important to conduct risk analysis on such accidents to support maritime safety management. In this paper, a modified ship collision candidate detection method is proposed as a tool for collision risk analysis in ports and waterways. Time‐Discrete Velocity Obstacle algorithm (TD‐NLVO) is utilized to detect collision candidates based on the encounter process extracted from AIS data. Ship domain model was further integrated into the algorithm as the criteria for determination. A case study is conducted to illustrate the efficacy of the improved model, and a comparison between the existing method and actual ship trajectories are also performed. The results indicate that with the integration of ship domain, the new method can effectively detect the encounters with significant collision avoidance behaviours. The choice of criteria can have a significant influence on the results of collision candidate detection.

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Maritime accidents, especially ship collisions, have always been a threat to the safety of maritime transport industry, the regional and global economy, and societies, due to its dire consequences. In this paper, a novel method to model causational factors, one of the critical elements of probabilistic risk modelling of ship collision accidents, is proposed. A credal probabilistic graphical network model based on imprecise probabilities was established based on accident investigation reports and domain experts as the overall framework to represent expert knowledge and probabilistic inference under uncertainty. Causational probability is estimated from the micro-to-macroscopic perspective where information of ship encounters are integrated into the causational model to perform probabilistic inference on each encounter and to obtain collective results. The causation probability interval is obtained and compared between model with and without the availability of geometric encounter data. The results indicate that: (1) the encounter information (relative bearing, TCPA, and presence of other ship) has influence on causational probability of ship collision accident to certain extent; human and organisational factors play more significant role; and (2) with AIS data integration, causational probability analysis can be utilized to determine encounters with higher likelihood and obtain details of dangerous ship encounters in regional maritime traffic.

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Following the growth in global trade activities, vessel traffic has increased dramatically in some busy waterways and ports. However, such increments have made it more complex to manage the regional vessel traffic, which can increase the risk of an accident in the area. To model and analyze the relationship between vessel traffic and maritime traffic, this paper proposes a gridded geography information system (GIS)-based relation analysis model using the historical automatic identification system (AIS) data and accident records over a 10-year-span. Firstly, the extent of the hazards posed by a maritime accident in terms of hull loss, fatality, and direct economic loss is quantified using set pair analysis. Consequently, the hazardous degree posed by an accident is obtained. The relative consequence of the regional hazard (RCORH) is then estimated by summing up all the relative hazardous degrees of accidents that have occurred in a certain gridded area. Secondly, the vessel traffic in the gridded areas is analyzed using characteristics such as speed, heading variance, and traffic volume as indicators. Based on the analysis of both the maritime traffic accidents and the vessel traffic, the spatial relationships are analyzed with an overlay between the RCORH and vessel traffic data of each grid, as well as a regression analysis. In a case study of the Western port of Shenzhen City, China, the methodology proves to be effective for vessel traffic management and traffic engineering design.

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Maritime transportation system has made a significant contribution to the development of the world economy. However, with the growth of quantity, scale, and speed of ships, maritime accidents still pose incrementing risk to individuals and societies in terms of multiple aspects, especially collision accidents between ships. Great effort is needed to prevent the occurrence of such accidents and to improve navigational safety and traffic efficiency. In this paper, extensive literature on probabilistic risk analysis on ship-ship collision was collected and reviewed focusing on the stakeholders which may benefit from the research and the methodologies and criteria adopted for collision risk. The paper identifies stakeholders, the modelling aspects (frequency estimation, causation analysis, etc.) in which the stakeholders are interested in. A classification system is presented based on the technical characteristics of the methods, followed by detailed descriptions of representative approaches and discussion. Areas for improvement of such risk analysis approaches are highlighted, i.e. identifying collision candidates, assessing the collision probability of multiple ships encounters, assessing the human and organizational factors. Three findings are concluded from this literature review: (1) Research on collision risk analysis and evaluation of ship encounters from individual ship perspective have facilitated the research in macroscopic perspective, and in turn, results from macroscopic research can also facilitate individual risk analysis by providing regional risk characteristics; (2) Current approaches usually estimate geometric probability by analysing data at certain intervals, which could lead to over/underestimation of the results; and (3) For causation probability induced by human and organisational factors in collision accidents, lack of data and uncertainty is still a problem to obtain accurate and reliable estimations. The paper also includes a discussion with respect to the applicability of the methods and outlines further work for improvement. The results in this paper are presented in a systematic structure and are formulated in a conclusive manner. This work can potentially contribute to developing better risk models and therefore better maritime transportation systems.@en

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.@en