Article

Adaptive collision avoidance decision system for autonomous ship navigation

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... One of the cores for MASS operating in restricted waters is ensuring safe navigation in an autonomous manner [5]- [7]. The autonomous control framework typically operates on two hierarchical levels: (i) a high-level decision-making layer, responsible for identifying feasible routes and selecting the optimal route based on performance evaluations [8], [9]; and This work was supported in part by the National Natural Science Foundations of China under Grant 52101361. ...
Preprint
Full-text available
To enhance the safety of Maritime Autonomous Surface Ships (MASS) navigating in restricted waters, this paper aims to develop a geometric analysis-based route safety assessment (GARSA) framework, specifically designed for their route decision-making in irregularly shaped waterways. Utilizing line and point geometric elements to define waterway boundaries, the framework enables to construct a dynamic width characterization function to quantify spatial safety along intricate waterways. An iterative method is developed to calculate this function, enabling an abstracted spatial property representation of the waterways. Based on this, we introduce a navigational safety index that balances global navigational safety and local risk to determine the safest route. To accommodate ship kinematic constraints, path modifications are applied using a dynamic window approach. A case study in a simulated Port of Hamburg environment shows that GARSA effectively identifies safe routes and avoids the risk of entering narrow waterways in an autonomous manner, thereby prioritizing safety in route decision-making for MASS in confined waters.
Article
Full-text available
Although many studies have focused on the occurrence likelihood of marine accidents, few have focused on the analysis of the severity of the consequences, and even fewer on the prediction of the severity. To this end, a new research framework is proposed in this study to accurately predict the severity of marine accidents. First, a novel two-stage feature selection (FS) method was developed to select and rank Risk Influential Factors (RIFs) to improve the accuracy of the Machine Learning (ML) model and interpretability of the FS. Second, a comprehensive evaluation method is proposed to measure the performance of the FS methods based on stability, predictive performance improvement, and statistical tests. Third, six well-established ML models were used and compared to measure the performance of different predictors. The Light Gradient Boosting Machine (LightGBM) was found to have the best predictive performance for the severity prediction of marine accidents and was treated as the benchmark model. Finally, LightGBM was used to predict accident severity based on the RIFs selected by the proposed FS method, and the effect of risk control measures was counterfactually analysed from a quantitative perspective. This innovative study on the use of improved ML approaches can effectively analyse and predict the severity of marine accidents, providing a novel methodology for and triggering a new direction for using Artificial Intelligence (AI) technologies in safety assessment and accident prevention studies. The source code is publicly available at: https://github.com/FengYinLeo/PGI-SDMI.
Article
Full-text available
Ship autonomous collision avoidance has attracted increasing attention in recent years. However, more attention is paid to the scenario in which the target ship keeps its course and speed. Less attention is paid to the development of a collision avoidance decision-making system under the uncertainty of the target ship's movement in a complex multi-ship encounter situation. Based on the idea of model predictive control (MPC), this paper proposes an autonomous ship collision avoidance decision-making system (CADMS) suitable for the uncertainty of ship motion. The CADMS includes four modules: collision risk analysis module, control and execution module, ship trajectory prediction module and collision avoidance decision-making model. The proposed model can be implemented in the collision avoidance decision-making system for safe navigation or it can be included in the ship autonomous navigation process. The decision-making model is achieved from a risk identification-motion prediction-ship control-scheme implementation perspective, and the dynamic and uncertainty features of the ship action (i.e., alter course or change speed) are considered in the modelling process. The real-time rolling update of ship collision avoidance decisions is realised based on the time series rolling method. Four scenarios are designed to demonstrate the collision avoidance decision-making system's performance. The results show that the proposed collision avoidance decision-making system is a reasonable and effective system for collision avoidance, particularly in multi-ship encounter situations of target ships suddenly altering course or change speed.
Article
Full-text available
Ship collisions are the primary threat to traffic safety in the sea, which can seriously threaten human lives, the environment and material assets. Therefore, the detection and analyze of ship collision risks have important theoretical significance and application value. To improve maritime safety and efficiency, we propose a modeling, visualization and prediction framework to analyze ship collision risk. In particular, to fully consider the maneuverability of the ship, we introduce the quaternion ship domain (QSD) into the vessel conflict ranking operator (VCRO). In addition, to further analyze and better understand collision risk, the kernel density estimation (KDE) model is employed to visualize the ship collision risk. The ship collision risk usually contains underlying patterns and laws. Thus, we proposed a convolutional long short-term memory network (ConvLSTM) model, which can extract spatial-temporal features and predict spatial-temporal risk. Finally, to verify the reliability and robustness of the framework, we conducted extensive experiments on the automatic identification system (AIS) data of Chengshantou water. The results show that the framework demonstrates superiority in risk calculation, visualization and prediction. Theoretically, the framework proposed in this paper can serve maritime intelligent transportation system well.
Article
Full-text available
Collision prevention is critical for navigational safety at sea, which has developed rapidly in the past decade and attracted a lot of attention. In this article, an improved velocity obstacle (IVO) algorithm for intelligent collision avoidance of ocean-going ships is proposed in various operating conditions, taking into count both a ship’s manoeuvrability and Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). An integrated model combines a three-degree-of-freedom manoeuvring model with ship propeller characteristics to provide a precise prediction of ships in various manoeuvring circumstances. In the given case, what is different to present studies, this improved algorithm allows for decision-making in two ways: altering course and changing speed. The proposed technique is demonstrated in a variety of scenarios through simulation. The findings reveal that collision-avoidance decision-making can intelligently avoid collisions with the target ships (TSs) in multi-ship situations.
Article
Full-text available
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.
Article
Full-text available
Conflict detection is a vital step of collision prevention at sea, determining if there is a risk of collision and when to take preventing actions. This article proposes a practical Rule-aware Time-varying Conflict Risk (R-TCR) for ship collision avoidance. Considering maritime practice, the conflict risk measure takes the ship maneuverability, the COLREGs, and good seamanship into account in the conflict risk measure. Specifically, the conflict risk is formulated as a ratio of achievable maneuvers leading to a collision to all achievable maneuvers. Simulations are carried out to show the characteristics of R-TCR. The results show that the R-TCR evaluates the entire conflict risk incorporating COLREG rules, multiple targets, different maneuverability, and varying ship domains. Finally, the proposed measure is applied to analyze the collision accident between two ships. Compared with the conventional risk indicators, the proposed R-TCR can deliver extra information to users, such as providing early warning, showing the room-for-maneuver, and suggesting evasive actions. Besides, the extra information also supports collision avoidance for autonomous ships.
Article
Full-text available
Ship collision prevention has always been a hot topic of research for navigation safety. Recently, autonomous ships have gained much attention as a means of solving collision problems by machine control with a collision-avoidance algorithm. An important question is how to determine optimal path planning for autonomous ships. This paper proposes a path-planning method of collision avoidance for multi-ship encounters that is easy to realize for autonomous ships. The ship course-control system uses fuzzy adaptive proportion-integral-derivative (PID) control to achieve real-time control of the system. The automatic course-altering process of the ship is predicted by combining the ship-motion model and PID controller. According to the COLREGs, ships should take different actions in different encounter situations. Therefore, a scene-identification model is established to identify these situations. To avoid all the TSs, the applicable course-altering range of the OS is obtained by using the improved velocity obstacle model. The optimal path of collision avoidance can be determined from an applicable course-altering range combined with a scene-identification model. Then, the path planning of collision avoidance is realized in the multi-ship environment, and the simulation results show a good effect. The method conforms to navigation practice and provides an effective method for the study of collision avoidance.
Article
Full-text available
In order to solve the problem of insufficient search ability of the unmanned surface vehicle (USV) collision avoidance planning algorithm, this paper proposes an improved ant colony optimization algorithm (ACO). First, aiming at the static unknown environment, in order to improve the real-time performance of USV online planning, and considering the environmental characteristics of USV operation for improving ACO to search for the optimal path, a dynamic viewable method is proposed for the local environment model. Second, according to the known dynamic environment, based on the motion velocity model and International Regulations for Preventing Collisions at Sea (COLREGS), a reverse eccentric expansion method is designed to deal with the dynamic obstacles. Then, aiming at the problem that ACO has a slow convergence speed, an improved pseudo-random proportional rule is proposed to select the ant state transition. And the wolf pack allocation principle and the maximum-minimum ant system are used to update the global pheromone to avoid the search falling into local optimum. Finally, the convergence, real-time performance, and stability of the improved ACO are verified through the simulation experiment of USV collision avoidance in the static unknown and dynamic known environment.
Article
Full-text available
The unmanned surface vehicle (USV) has been widely used to accomplish missions in the sea or dangerous marine areas for ships with sailors, which greatly expands protective capability and detection range. When USVs perform various missions in sophisticated marine environment, autonomous navigation and obstacle avoidance will be necessary and essential. However, there are few effective navigation methods with real-time path planning and obstacle avoidance in dynamic environment. With tailored design of state and action spaces and a dueling deep Q-network, a deep reinforcement learning method ANOA (Autonomous Navigation and Obstacle Avoidance) is proposed for the autonomous navigation and obstacle avoidance of USVs. Experimental results demonstrate that ANOA outperforms deep Q-network (DQN) and Deep Sarsa in the efficiency of exploration and the speed of convergence not only in static environment but also in dynamic environment. Furthermore, the ANOA is integrated with the real control model of a USV moving in surge, sway and yaw and it achieves a higher success rate than Recast navigation method in dynamic environment.
Article
Full-text available
In this article, a ship manoeuvrability-based simulation for ship navigation in collision situations is established. Under the general requirement from the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) and good seamanship, the determination of encounter situations is quantified to reduce navigators’ intervention. Meanwhile, the action manner by course alteration or changing speed in some typical encounter situations is graphically analysed for both the give-way and stand-on vessels. Then, the multiple genetic algorithm and linear extension algorithm are adopted to perform trajectory planning for collision avoidance. To improve the reliability of the simulation system, the mathematical model of ship motion and ship manoeuvring control mechanism are adopted, which can eliminate the insufficiency of neglect of ship manoeuvrability in the process of collision avoidance. Meanwhile, the course encoding technique is adopted to fit the ship manoeuvring control mechanism. Finally, a set of traffic scenarios emulating different encounter situations are applied to demonstrate the effectiveness, consistency, and practicality of this system.
Article
Full-text available
Numerous methods have been developed for ship collision prevention over the past decades. However, most studies are based on strong assumptions, such as the need for a constant velocity of the target-ship, the limitation to two-ship scenarios, the simplification of ships’ dynamics, etc. Generalized Velocity Obstacle (GVO) algorithm can bridge these gaps. This paper presents a GVO algorithm for ship collision avoidance and designs a collision avoidance system (GVO-CAS). The proposed system visualizes the changes of one ship’s course and speed resulting in collisions, which can be used not only for supporting the officer on watch to prevent collisions, but also for collision prevention of Autonomous Surface Vessels (ASVs) and for human operators taking over the control of ASVs. Simulation experiments show that the proposed collision avoidance system can work properly in various maritime environments. Compared to the original Velocity Obstacle algorithm, the GVO algorithm is more reliable and suitable for close range ship collision avoidance. Moreover, the GVO-CAS can offer rule-compliant evasive actions with a minimum number of required actions for ships. These results show the great potential to use the GVO algorithm in both manned and unmanned ships at sea.
Article
The field of ship autonomous navigation has always garnered significant interest due to its future development potential for intelligent ships and unmanned ships. While there has been extensive research on autonomous navigation in open waters, less focus has been given to coastal waters due to the complexity of the environment and traffic flow. In order to resolve this problem, the dynamic adaptive decision-making method for ship autonomous navigation in coastal waters is presented. A digital twin environment model tailored to the characteristics of coastal waters has been developed, which can dynamically replicate the current ship navigation environment by incorporating multi-source heterogeneous information from ship equipment. The autonomous navigation decision-making method is obtained by integrating an Improved Velocity Obstacle (IVO) for ship collision avoidance and a Line of Sight (LOS) algorithm for ship trajectory tracking. Moreover, a time-rolling algorithm is employed to facilitate specific navigation decision-making in time-varying environments and to account for the uncertainty of target ship motion over time. This comprehensive algorithm has been tested and validated in two different scenarios. The results demonstrate that the proposed navigation decision-making method is reasonable and effective for the ship navigating in the coastal water, particularly in multi-ship encounter situations of target ships suddenly altering course or changing speed.
Article
Ship collision avoidance has always been a concern and it is crucial for achieving safe navigation of ships at sea. There are many studies on ship collision avoidance in open water, but less attention on coastal waters considering the uncertainty of target ships due to the complexity of the environment and traffic flow. In this paper, collision avoidance decision-making research in coastal waters considering the uncertainty of target ships was proposed. Firstly, accurate ship trajectories are obtained by preprocessing the raw Automatic Identification System (AIS) data. Subsequently, the processed trajectories are clustered using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm and Hausdorff distance, acquiring a dataset for trajectory prediction of target ships. Then, a mixed Gaussian model is utilized to calculate the prior probability distribution of the prediction model, thus establishing a trajectory prediction model that considers the uncertainty of the target ship. Finally, ship maneuverability is simulated using the Mathematical Model Group (MMG) and Proportion Integration Differentiation (PID) models, and a collision avoidance decision-making model for ships is constructed. The proposed algorithm has been tested and verified in a case study. The results show that the approach effectively predicts the trajectory of the target ship and facilitates informed collision avoidance decision-making.
Article
The maritime industry is following the trend of increased autonomy and digitalisation applied in aviation, automotive, military, and chemical industries. Maritime autonomous and unmanned vehicles have received significant attention recently, both from academia and industry. This paper investigates research into the progress of the development of autonomous and unmanned shipping by employing bibliometric analysis tools VOSviewer and CiteSpace, to present a comprehensive picture of this emerging field of research. Bibliometrics is applied to investigate the collected data sample from Scopus related to predefined keywords. Bibliometric tools assist review by network visualisation, clustering, and metrics. Therefore, this paper presents an analysis aiming at (1) increasing the understanding of the structure and contents of the academic field of autonomous and unmanned shipping; (2) determining and mapping scientific networks in this field; (3) analysing and visualising major divisions within the field; (4) identifying research needs and future research directions in the field. Through clusters generated by bibliometric tools, research divisions are identified and discussed. Furthermore, potential research directions are outlined.
Article
In this paper, a novel swarm control strategy based path following guidance is addressed for multiple underactuated surface vehicles (USVs) with complex unknowns including uncertain dynamics and time-varying disturbances. By employing swarm center position (SCP) guidance together with neural network approximators, the distributed robust controller is developed for each vehicle to follow the desired path, and guarantees that path following errors converge to a small neighborhood of origin. An improved artificial potential field (APF) is further responsible for collision avoidance, which makes vehicles bypass obstacles smoothly. The superiority of the proposed swarm control strategy is that the distributed controller and flexible formation enable USVs to follow the target and avoid collisions autonomously. Simulation results are proposed to illustrate the universal applicability and effectiveness of the proposed strategy.
Article
It is extremely challenging to carry out an advanced adaptive navigation system in the complex environment of a port. Especially in the waters that are about to be berthed, the unmanned surface vehicles (USVs) should autonomously identify and avoid dense buoys according to the rules. Each country has corresponding mandatory documents that strictly regulate the shape, color and orientation of water buoys in coastal and estuary channels. Aiming at the problem of USV collision avoidance navigation under the constraints of the IALA (International Association of Marine Aids and Lighthouse Authorities) Maritime buoyage system, this paper designs a mixed environment model including multiple ocean buoys according to this rule. By using the deep neural network to extract the state features of the target, and setting the reward function reasonably, the USV can not only navigate to the target autonomously, but also identify the corresponding buoy and give the corresponding decision. Using different DQN optimization algorithms to conduct comparative experiments, the stability of the algorithm's learning of the optimal strategy is improved. The results show that the algorithm can accurately avoid obstacles ahead, identify buoys effectively, and realize effective autonomous collision avoidance decision-making in complex environments with static obstacles and buoys with indicating function. This research can provide theoretical basis and method reference for USV's autonomous navigation in port.
Article
The ship domain concept is of great interest for ship traffic modelling, risk assessment and intelligent collision avoidance. The paper proposes and applies a method to define AIS data-based empirical polygonal ship domains, based on traffic density matrices that are derived around each reference ship from an AIS dataset. A modified Quaternion Ship Domain allowing for different shapes for each quadrant is proposed, which results in a better fitting to the empirical domain. The parameters of Quaternion Ship Domains that best fit the empirical polygonal domains are determined for cargo ships and tankers of different lengths. Violations of the Quaternion Ship Domain are then used as an indicator of collision risk that is graphically represented in the study area, providing important information to support maritime traffic monitoring and control tasks.
Article
Social interest in autonomous navigation systems for autonomous ships is also increasing. For a robust autonomous navigation system, the location, speed, and direction of the ship and other ships must be identified in real time, and collision avoidance should be performed at an appropriate time by considering the collision risk. In this study, we proposed a collision avoidance method that quantitatively assesses the collision risk and then generates an avoidance path. First, to assess the collision risk, a collision risk assessment method based on the ship domain and the closest point of approach (CPA) was proposed. The ship domain is created with an asymmetric shape considering manoeuvring performance and the COLREGs. The CPA is used to assess quantitative collision risk value. Subsequently, a path generation algorithm based on deep reinforcement learning (DRL) was proposed to determine the avoidance time and to generate an avoidance path complying the COLREGs for the most dangerous ship in terms of collision risk. The information of own ship and target ship such as location, speed, heading, collision risk is used as the input state, and the rudder angle of own ship is set as the output action of the DRL. The cost function related to the path following and the collision avoidance is defined as the reward of the DRL-based collision avoidance method. Additionally, the DRL modes are defined to navigate the flexible avoidance path by changing the ratio between the path following and the collision avoidance. To verify the proposed method, we compared the collision avoidance method with the A* algorithm, which is a traditional path planning algorithm, and analyzed the results for various scenarios. The proposed method reliably avoided collisions through flexible paths for complex and unexpected changes in situations compared to the A* algorithm.
Article
It is vital to analyse ship collision risk for preventing collisions and improving safety at sea. The state-of-the-art of ship collision risk analysis focuses on encountering conflict between ship pairs, subject to a strong assumption of the ships having no/little spatiotemporal motion uncertainty. This paper proposes a probabilistic conflict detection approach to estimate potential collision risk of various multi-vessel encounters, in which the spatiotemporal-dependent patterns of ship motions are newly taken into account through quantifying the trajectory uncertainty distributions using AIS data. The estimation accuracy and efficiency are assured by employing a two-stage Monte Carlo simulation algorithm, which provides the quantitative bounds on the approximation accuracy and allows for a fast estimation of conflict criticality. Several real experiments are conducted using the AIS-based trajectory data in Ningbo-Zhoushan Port to demonstrate the feasibility and superiority of the proposed new approach. The results show that it enables the effective detection of collision risk timely and reliably in a complicated dynamic situation. They therefore provide valuable insights on ship collision risk prediction as well as the formulation of risk mitigation measures.
Article
The wider use of electronic devices in shipping has led to the introduction of new navigation control systems and decision support tools for collision avoidance. The application of these systems improves the data utilisation and allows for the prediction of ship's performance in various operational conditions. In this study, a decision support methodology for collision avoidance of ocean going vessels is developed, taking into account both the ship's manoeuvrability and propulsion system performance whilst employing a new formulation for the estimation of the collision probability indicator. An integrated model that simulates the ship's manoeuvrability and propulsion system performance is employed to predict the required ship's hull and propulsion plant dynamic response. The integrated model couples a 3-DOF manoeuvring model with a mean value engine model, providing a fast but accurate prediction of ship and her propulsion system characteristics during specific manoeuvring scenarios. The derived ship trajectories populate a database that is employed as input to the developed decision support for investigating various encounter situations depending on the target ship's initial position, approach angle, trajectory and speed. The developed decision support provides the collision probability indicator as function of the ordered rudder angle and engine speed as the available control options to avoid the collision. This study provides support to the officer of the watch to make decisions on the ship and propulsion system control parameters during encountering situations, thus contributing to the safer maritime operations.
Article
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.
Article
A novel scheme is proposed for the distributed multi-ship collision avoidance (CA) problem with consideration of the autonomous, dynamic nature of the real circumstance. All the ships in the envisioned scenario cannot share their decisions or motivation, and they make decisions based on the limited observable information. Each ship is assumed to have a high-layer intention to guide the CA decision, which is called the collision avoidance logic (CAL). Each ship has its own CAL that governs the CA decisions and actions; meanwhile, each ship tries to understand the CALs of other ships by continuous inference and observation according to their extrinsic behaviours, especially the difference between the observed information and the predicted behaviour. This iterative scheme features a four-phase, programmed decision-making procedure, namely the observation-inference-prediction-decision (OIPD) model. Simulation results indicate that the OIPD method shows good flexibility and adaptability. When all ships comply COLREGs, the proposed scheme can produce normal solutions. In case some of the ships fail to make correct decisions, the other ships that adopt the proposed scheme can response in time and revise their CA strategies in a proactive way. Data Envelopment Analysis method is applied to further quantitatively evaluate the efficiency of the proposed scheme.
Article
This paper proposes a fuzzy logic-based intelligent decision-making approach for navigation strategy selection in the inland traffic separation scheme. The dynamic characteristics of navigation process, including free navigation, ship following and ship overtaking, are further analysed. The proposed model can be implemented in the decision support system for safe navigation or be included in the process of autonomous navigation. The decision-making model is achieved from the perception-anticipation-inference-strategy perspective, and the dynamic features of ships (i.e. speed, distance, and traffic flow) are comprehensively considered in the modelling process. From the results of both scenarios for overtaking and following, it illustrates that the timing is significant for strategy selection and should well consider the complex situation and ship behaviours, moreover, the proposed approach can be used for intelligent strategy selection.
Article
Each vessel has its own way of sailing in the port region. Any autonomous vessel navigating such a scene should be able to predict the trajectories of surrounding ships and adjust its behaviour to avoid a collision. In this paper, combined with the sequence prediction method, a Long Short-Term Memory (LSTM) model is proposed to predict the trajectories of the vessels. The ground-truth Automatic Identification System (AIS) data in the port of Tianjin, China are used to train and test the proposed model. The experimental results prove that our model can predict ship trajectories accurately, and it is applicable to the autonomous navigation system.
Article
Maritime shipping transports about 90% of the international trade and is considered a high-risk industry. Among all daily operations carried out by merchant ships, navigation is deemed the most critical. With the ever-increasing dimensions of ocean-going ships and the threat possessed by dangerous cargoes in the event of an accident to individual lives and environment alike, the human factors which affect the navigation of merchant ships require paramount consideration to enhance safety in maritime operations. In this study, we explore the concept of Situation Awareness (SA) within the maritime domain, identifying the SA information requirements of navigators and factors affecting their SA. A total of 7 experienced navigators were interviewed in this exploratory study to determine the SA information requirements by a Goal-Directed Task Analysis (GDTA). Three Subject Matter Experts (SMEs) possessing Captain’s license for merchant ships were used for validation of initial findings. The findings reveal the information navigators use during the pilotage phase of navigation and further classify them in three levels. The findings shed light on the factors that affect SA of navigators and are discussed with their potential implications for the procedures and practices which better support SA in maritime navigation.
Article
This paper introduces an approach for solving a safe ship trajectory planning problem. The algorithm, utilising the concept of a discrete artificial potential field and a path optimisation algorithm, calculates an optimised collision-free trajectory for a ship. The method was validated by simulation tests with the use of real navigational data registered on board the research and training ship Horyzont II . Results of simulation studies demonstrate that the approach is capable of finding a collision-free trajectory in near-real time, and this proves its applicability in commercial collision avoidance systems for ships. The paper contributes to the development of decision support systems for ships and autonomous navigation.
Article
The uncertainty of ship trajectory prediction is addressed. In particular, a probabilistic trajectory prediction model is proposed that describes the uncertainty in future positions along the ship trajectories by continuous probability distributions. The ship motion prediction is decomposed into lateral and longitudinal directions, and position probabilities are calculated along these two directions. A data-driven non-parametric Bayesian model based on a Gaussian Process is proposed to describe the lateral motion uncertainty, while the longitudinal uncertainty results from the uncertainty on the ship acceleration along the route. The parameters of the probabilistic models are derived off-line based on historic trajectory information provided by Automatic Identification System (AIS) data. The model is then applied to predict the trajectory uncertainty in real time by iteratively updating the prior probability models based on new observed AIS data. Moreover, a sequential Cholesky decomposition algorithm is applied in this study to reduce the computational effort required by the Gaussian Process modelling. Three months of AIS data are used to train and test the proposed probabilistic trajectory prediction model. The results obtained show that the proposed method has high prediction accuracy and meets the demands of real-time applications.
Article
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.
Article
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.
Article
Multi-vessel collision risk assessment for maritime traffic surveillance is a key technique to ensure the safety and security of maritime traffic and transportation. This paper proposes a framework of real-time multi-vessel collision assessment that combines a spatial clustering process (DBSCAN) for detecting clusters of encounter vessels and a multi-vessel collision risk index model for encounter vessels within each cluster from the large amounts of monitored vessels in a surveyed sea area. First, the vessels monitored are clustered using DBSCAN to obtain the clusters of encounter vessels, filtering out the relatively safe vessels. Then, the dynamic motion relation between encounter vessels within each cluster is modeled to obtain DCPA and TCPA. The semantic and mathematical relationship of vessel collision risk index for each cluster of encounter vessels with DCPA and TCAP is constructed using a negative exponential function. To illustrate the effectiveness of the framework proposed, an experimental case study has been carried out within the west coastal waters of Sweden. The results show that our framework is effective and efficient at detecting and ranking collision risk indexes between encounter vessels within each cluster, which allows an automatic risk prioritization of encounter vessels for further investigation by operators. Hence, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of lives and property.
Article
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.
Article
This paper addresses two interrelated problems concerning the planar three degree-of-freedom motion of a vehicle, namely, the path planning problem and the guidance problem. The monotone cubic Hermite spline interpolation (CHSI) technique by Fritsch and Carlson is employed to design paths that provide the user with better shape control and avoid wiggles and zigzags between the two successive waypoints. The conventional line-of-sight (LOS) guidance law is modified by proposing a time-varying equation for the lookahead distance, which is a function of the cross-track error. This results in a more flexible maneuvering behavior that can contribute to reaching the desired path faster as well as obtaining a diminished oscillatory behavior around the desired path. The guidance system along with a heading controller form a cascaded structure, which is shown to be κ-exponentially stable when the control task is to converge to the path produced by the aforementioned CHSI method. In addition, the issue of compensating for the sideslip angle β is discussed and a new κ-exponentially stable integral LOS guidance law, capable of eliminating the effect of constant external disturbances for straight-line path following, is derived.