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Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships

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A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 29179

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Special Issue Editors

Prof. Dr. Yuanqiao Wen

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Guest Editor

National Engineering Research Center Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: nautical traffic safety and simulation; artificial intelligence and its applications in maritime; maritime autonomous surface ships
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Axel Hahn

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Guest Editor

German Aerospace Center e.V., University of Oldenburg, 6121 Oldenburg, Germany
Interests: simulation; maritime systems; transportation; navigation
Special Issues, Collections and Topics in MDPI journals

Dr. Osiris Valdez Banda

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Guest Editor

Research Group on Safe and Efficient Marine Systems, Marine Technology, Department of Mechanical Engineering, Aalto University, 02150 Espoo, Finland
Interests: safety and systems engineering; risk analysis; maritime safety; winter navigation; autonomous ships
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yamin Huang

E-Mail Website1 Website2
Guest Editor

National Engineering Research Center Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: human–machine cooperation; autonomous ships; ship collision avoidance; maritime traffic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding, modeling, and predicting ship behavior are fundamental and essential issues for planning, controlling, and operating different levels of maritime autonomous surface ships (MASS). The maritime traffic data (e.g., radar data, AIS data, CCTV data) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, which are a treasure for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multisource heterogeneous big data and artificial intelligence techniques inspire innovative and important means for the development of MASS, leading to smart, safe, green, and efficient shipping. Thus, this Special Issue aims to provide a medium to present the latest developments on methods and tools suitable for relevant issues, including but not limited to:

Data-driven behavior modeling and simulation;
Knowledge-driven behavior modeling and reasoning;
Multisource heterogeneous traffic data fusion;
Semantic analysis of ship behaviors;
Quantifying COLREGs and seamanship for machine;
Inference engine and ontology reasoning for rule-compliant MASS;
Intention inference based on behavior observations;
Maritime traffic situational awareness;
Multiagent simulation;
Risk analysis and management of MASS;
Safety and cybersecurity of MASS.

Prof. Dr. Yuanqiao Wen
Prof. Dr. Axel Hahn
Prof. Dr. Osiris Valdez Banda
Prof. Dr. Yamin Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Maritime Autonomous Surface Ships (MASS)
Data-driven modeling
Knowledge-driven modeling
Behavior modeling
Knowledge graph
Navigation simulation
Multisource heterogeneous data analysis
Nautical safety
Quantifying rules and regulations

Published Papers (14 papers)

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Editorial

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4 pages, 172 KiB

Open AccessEditorial

Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships

by Yuanqiao Wen, Axel Hahn, Osiris Valdez Banda and Yamin Huang

J. Mar. Sci. Eng. 2023, 11(3), 635; https://doi.org/10.3390/jmse11030635 - 17 Mar 2023

Viewed by 1221

Abstract

This Special Issue, “Data-/Knowledge-Driven Behavior Analysis of Maritime Autonomous Surface Ships”, includes twelve contributions [...] Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

Research

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21 pages, 5766 KiB

Open AccessArticle

Semantic Modeling of Ship Behavior in Cognitive Space

by Rongxin Song, Yuanqiao Wen, Wei Tao, Qi Zhang, Eleonora Papadimitriou and Pieter van Gelder

J. Mar. Sci. Eng. 2022, 10(10), 1347; https://doi.org/10.3390/jmse10101347 - 22 Sep 2022

Cited by 7 | Viewed by 1570

Abstract

Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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18 pages, 11874 KiB

Open AccessArticle

Available-Maneuvering-Margins-Based Ship Collision Alert System

by Lei Du, Osiris A. Valdez Banda and Zhongyi Sui

J. Mar. Sci. Eng. 2022, 10(8), 1123; https://doi.org/10.3390/jmse10081123 - 15 Aug 2022

Cited by 4 | Viewed by 1751 | Correction

Abstract

The timing of a ship taking evasive maneuvers is crucial for the success of collision avoidance, which is affected by the perceived risk by the navigator. Therefore, we propose a collision alert system (CAS) based on the perceived risk by the navigator to trigger a ship’s evasive maneuvers in a timely manner to avoid close-quarters situations. The available maneuvering margins (AMM) with ship stability guarantees are selected as a proxy to reflect the perceived risk of a navigator; hence, the proposed CAS is referred to as an AMM-based CAS. Considering the dynamic nature of ship operations, the non-linear velocity obstacle method is utilized to identify the presence of collision risk to further activate this AMM-based CAS. The AMM of a ship are measured based on ship maneuverability and stability models, and the degree to which they violate the risk-perception-based ship domain determines the level of collision alert. Several typical encounter scenarios are selected from AIS data to demonstrate the feasibility of this AMM-based CAS. The promising results suggest that this proposed AMM-based CAS is applicable in both ship pair encounter and multi-vessel encounter scenarios. Collision risk can be accurately detected, and then a collision alert consistent with the risk severity is issued. This proposed AMM-based CAS has the potential to assist autonomous ships in understanding the risk level of the encounter situation and determining the timing for evasive maneuvers. The advantages and limitation of this proposed method are discussed. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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21 pages, 4208 KiB

Open AccessArticle

Multi-Sensor-Based Hierarchical Detection and Tracking Method for Inland Waterway Ship Chimneys

by Fumin Wu, Qianqian Chen, Yuanqiao Wen, Changshi Xiao and Feier Zeng

J. Mar. Sci. Eng. 2022, 10(6), 809; https://doi.org/10.3390/jmse10060809 - 13 Jun 2022

Cited by 1 | Viewed by 1653

Abstract

In the field of automatic detection of ship exhaust behavior, a deep learning-based multi-sensor hierarchical detection method for tracking inland river ship chimneys is proposed to locate the ship exhaust behavior detection area quickly and accurately. Firstly, the primary detection uses a target detector based on a convolutional neural network to extract the shipping area in the visible image, and the secondary detection applies the Ostu binarization algorithm and image morphology operation, based on the infrared image and the primary detection results to obtain the chimney target by combining the location and area features; further, the improved DeepSORT algorithm is applied to achieve the ship chimney tracking. The results show that the multi-sensor-based hierarchical detection and tracking method can achieve real-time detection and tracking of ship chimneys, and can provide technical reference for the automatic detection of ship exhaust behavior. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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24 pages, 6583 KiB

Open AccessArticle

A Novel Decision Support Methodology for Autonomous Collision Avoidance Based on Deduction of Manoeuvring Process

by Ke Zhang, Liwen Huang, Xiao Liu, Jiahao Chen, Xingya Zhao, Weiguo Huang and Yixiong He

J. Mar. Sci. Eng. 2022, 10(6), 765; https://doi.org/10.3390/jmse10060765 - 01 Jun 2022

Cited by 13 | Viewed by 1929

Abstract

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. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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26 pages, 2812 KiB

Open AccessArticle

Use of Hybrid Causal Logic Method for Preliminary Hazard Analysis of Maritime Autonomous Surface Ships

by Di Zhang, Zhepeng Han, Kai Zhang, Jinfen Zhang, Mingyang Zhang and Fan Zhang

J. Mar. Sci. Eng. 2022, 10(6), 725; https://doi.org/10.3390/jmse10060725 - 25 May 2022

Cited by 9 | Viewed by 1935

Abstract

Recently, the safety issue of maritime autonomous surface ships (MASS) has become a hot topic. Preliminary hazard analysis of MASS can assist autonomous ship design and ensure safe and reliable operation. However, since MASS technology is still at its early stage, there are not enough data for comprehensive hazard analysis. Hence, this paper attempts to combine conventional ship data and MASS experiments to conduct a preliminary hazard analysis for autonomy level III MASS using the hybrid causal logic (HCL) method. Firstly, the hazardous scenario of autonomy level III MASS is developed using the event sequence diagram (ESD). Furthermore, the fault tree (FT) method is utilized to analyze mechanical events in ESD. The events involving human factors and related to MASS in the ESD are analyzed using Bayesian Belief Network (BBN). Finally, the accident probability of autonomy level III MASS is calculated in practice through historical data and a test ship with both an autonomous and a remote navigation mode in Wuhan and Nanjing, China. Moreover, the key influence factors are found, and the accident-causing event chains are identified, thus providing a reference for MASS design and safety assessment process. This process is applied to the preliminary hazard analysis of the test ship. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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17 pages, 3812 KiB

Open AccessArticle

Ship Intention Prediction at Intersections Based on Vision and Bayesian Framework

by Qianqian Chen, Changshi Xiao, Yuanqiao Wen, Mengwei Tao and Wenqiang Zhan

J. Mar. Sci. Eng. 2022, 10(5), 639; https://doi.org/10.3390/jmse10050639 - 07 May 2022

Cited by 2 | Viewed by 1905

Abstract

Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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20 pages, 4995 KiB

Open AccessArticle

Research on Ship Trajectory Classification Based on a Deep Convolutional Neural Network

by Tao Guo and Lei Xie

J. Mar. Sci. Eng. 2022, 10(5), 568; https://doi.org/10.3390/jmse10050568 - 22 Apr 2022

Cited by 8 | Viewed by 2235

Abstract

With the aim of solving the problems of ship trajectory classification and channel identification, a ship trajectory classification method based on deep a convolutional neural network is proposed. First, the ship trajectory data are preprocessed using the improved QuickBundle clustering algorithm. Then, data are converted into ship trajectory image data, a dataset is established, a deep convolutional neural network-based ship trajectory classification model is constructed, and the manually annotated dataset is used for training. The fully connected neural network model and SVM model with latitude and longitude data as input are selected for comparative analysis. The results show that the ship trajectory classification model based on a deep convolutional neural network can effectively distinguish ship trajectories in different waterways, and the proposed method is an effective ship trajectory classification method. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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19 pages, 2578 KiB

Open AccessArticle

Motion Planning for an Unmanned Surface Vehicle with Wind and Current Effects

by Shangding Gu, Chunhui Zhou, Yuanqiao Wen, Changshi Xiao and Alois Knoll

J. Mar. Sci. Eng. 2022, 10(3), 420; https://doi.org/10.3390/jmse10030420 - 14 Mar 2022

Cited by 7 | Viewed by 2273

Abstract

Aiming at the problem that unmanned surface vehicle (USV) motion planning is disturbed by effects of wind and current, a USV motion planning method based on regularization-trajectory cells is proposed. First, a USV motion mathematical model is established while considering the influence of wind and current, and the motion trajectory is analyzed. Second, a regularization-trajectory cell library under the influence of wind and current is constructed, and the influence of wind and current on the weight of the search cost is analyzed. Finally, derived from the regularization-trajectory cell and the search algorithm, a motion planning method for a USV that considers wind and current effects is provided. The experimental results indicate that the motion planning is closer to the actual trajectory of a USV in complex environments and that our method is highly practicable. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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18 pages, 4117 KiB

Open AccessArticle

Ship Traffic Flow Prediction in Wind Farms Water Area Based on Spatiotemporal Dependence

by Tian Xu and Qingnian Zhang

J. Mar. Sci. Eng. 2022, 10(2), 295; https://doi.org/10.3390/jmse10020295 - 21 Feb 2022

Cited by 10 | Viewed by 2062

Abstract

To analyze the changing characteristics of ship traffic flow in wind farms water area, and to improve the accuracy of ship traffic flow prediction, a Gated Recurrent Unit (GRU) of a Recurrent Neural Network (RNN) was established to analyze multiple traffic flow sections in complex waters based on their traffic flow structure. Herein, we construct a spatiotemporal dependence feature matrix to predict ship traffic flow instead of the traditional ship traffic flow time series as the input of the neural network. The model was used to predict the ship traffic flow in the water area of wind farms in Yancheng city, Jiangsu Province. Autoregressive Integrated Moving Average (ARIMA), Support-Vector Machine (SVM) and Long Short-Term Memory (LSTM) were chosen as the control tests. The GRU method based on the spatiotemporal dependence is more accurate than the current mainstream ship traffic flow prediction methods. The results verify the reliability and validity of the GRU method. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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20 pages, 10299 KiB

Open AccessArticle

Ontological Ship Behavior Modeling Based on COLREGs for Knowledge Reasoning

by Shubin Zhong, Yuanqiao Wen, Yamin Huang, Xiaodong Cheng and Liang Huang

J. Mar. Sci. Eng. 2022, 10(2), 203; https://doi.org/10.3390/jmse10020203 - 02 Feb 2022

Cited by 10 | Viewed by 2450

Abstract

Formal expression of ship behavior is the basis for developing autonomous navigation systems, which supports the scene recognition, the intention inference, and the rule-compliant actions of the systems. The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) offers experience-based expressions of ship behavior for human beings, helping the humans recognize the scene, infer the intention, and choose rule-compliant actions. However, it is still a challenge to teach a machine to interpret the COLREGs. This paper proposed an ontological ship behavior model based on the COLREGs using knowledge graph techniques, which aims at helping the machine interpret the COLREGs rules. In this paper, the ship is seen as a temporal-spatial object and its behavior is described as the change of object elements in time spatial scales by using Resource Description Framework (RDF), function mapping, and set expression methods. To demonstrate the proposed method, the Narrow Channel article (Rule 9) from COLREGs is introduced, and the ship objects and the ship behavior expression based on Rule 9 are shown. In brief, this paper lays a theoretical foundation for further constructing the ship behavior knowledge graph from COLREGs, which is helpful for the complete machine reasoning of ship behavior knowledge in the future. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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17 pages, 5171 KiB

Open AccessArticle

Collision Avoidance Algorithm for USV Based on Rolling Obstacle Classification and Fuzzy Rules

by Lifei Song, Xiaoqian Shi, Hao Sun, Kaikai Xu and Liang Huang

J. Mar. Sci. Eng. 2021, 9(12), 1321; https://doi.org/10.3390/jmse9121321 - 23 Nov 2021

Cited by 10 | Viewed by 2230

Abstract

Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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Review

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19 pages, 1364 KiB

Open AccessReview

Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

by Yingjie Tang, Junmin Mou, Linying Chen and Yang Zhou

J. Mar. Sci. Eng. 2022, 10(2), 139; https://doi.org/10.3390/jmse10020139 - 20 Jan 2022

Cited by 3 | Viewed by 2810

Abstract

Through the continuous development of intellectualization, considering the lifecycle of ships, the future of a waterborne traffic system is bound to be a mixed scenario where intelligent ships of different autonomy levels co-exist, i.e., mixed waterborne traffic. According to the three modules of ships’ perception, decision-making, and execution, the roles of humans and machines under different autonomy levels are analyzed. This paper analyzes and summarizes the intelligent algorithms related to the three modules proposed in the last five years. Starting from the characteristics of the algorithms, the behavior characteristics of ships with different autonomous levels are analyzed. The results show that in terms of information perception, relying on the information perception techniques and risk analysis methods, the ship situation can be judged, and the collision risk is evaluated. The risk can be expressed in two forms, being graphical and numerical. The graphical images intuitively present the risk level, while the numerical results are easier to apply into the control link of ships. In the future, it could be considered to establish a risk perception system with digital and visual integration, which will be more efficient and accurate in risk identification. With respect to intelligent decision-making, currently, unmanned ships mostly use intelligent algorithms to make decisions and tend to achieve both safe and efficient collision avoidance goals in a high-complexity manner. Finally, regarding execution, the advanced power control devices could improve the ship’s maneuverability, and the motion control algorithms help to achieve the real-time control of the ship’s motion state, so as to further improve the speed and accuracy of ship motion control. With the upgrading of the autonomy level, the ship’s behavior develops in a safer, more efficient, and more environment-friendly manner. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships)

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