A Collision Risk Identification Method for Autonomous Ships Based on Field Theory

Qiao, Zhenxing; Zhang, Yingjun; Wang, Shaobo

doi:10.1109/access.2021.3059248

Cited by 20 publications

(11 citation statements)

References 41 publications

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“…Some researchers tend to indicate the risk as a numerical index, such as the Minimum Safety Passing Distance (MSPD) [23,24] and the Collision Risk Index (CRI) [25][26][27][28][29], which is deemed as a numerical form of risk. Whereas, the other way to reflect risk is in two-dimensional graphics, such as Ship Domain (SD) [30][31][32][33], dangerous region (DR) [34][35][36], and action lines (AL) [37,38].…”

Section: Risk Perception Methodsmentioning

confidence: 99%

“…The results show that the accuracy of DDV/TDV is higher than that of the DCPA/TCPA. Qiao et al developed a quadratic ship domain model considering the uncertainty of ship position and proposed a method to calculate the spatial collision risk, which had been improved in further applications [31]. Some researchers assess the collision risk by developing new SD models.…”

Section: Risk Perception Methodsmentioning

confidence: 99%

“…[25][26][27][28][29] Graphical SD Deemed to be a risk of collision when TS enters or will enter SD. [30][31][32][33] DR Collect the set of speeds or courses that cause the OS to conflict with the TS, then displays the set in the image to the OOW.…”

Section: Numerical Mspdmentioning

confidence: 99%

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Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

Tang

Mou

Chen

et al. 2022

JMSE

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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.

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Section: Risk Perception Methodsmentioning

confidence: 99%

Section: Risk Perception Methodsmentioning

confidence: 99%

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Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

Tang

Mou

Chen

et al. 2022

JMSE

View full text Add to dashboard Cite

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“…The International Regulations for Preventing Collisions at Sea (COLREGS) contain rules that cannot be presented simply, clearly, and quantitatively, as in Go and chess. These regulations involve qualitative evaluation [32]; therefore, there are many ambiguous elements involved when artificial intelligence learns COLREGS on its own. For example, in terms of a summary of the safe speed in Rule 6 of COLREGS, it is stated that "Every vessel shall at all times proceed at a safe speed so that she can take proper and effective action to avoid collision and be stopped within a distance appropriate to the prevailing circumstances and conditions".…”

Section: Introductionmentioning

confidence: 99%

SASD Modeling Using an ANFIS to Prevent the Collision of MASS in Restricted Areas

Lee

Park

2022

JMSE

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This study aimed to present the criteria for the collision avoidance of maritime autonomous surface ships (MASS) in restricted areas. The criteria were presented via ship domains, and a seafarers’ awareness-based ship domain (SASD) that dynamically changes the existing SASD according to the external environment was used to develop a new domain model. For application, we have converged an adaptive neuro-fuzzy inference system with the existing model. The proposed SASD was applied to an actual marine restricted area and compared with automatic identification system (AIS) based ship domain models. Consequently, although there was a slight difference depending on the restricted area and domain design method applied, the SASD, (i.e., the minor axis of the elliptical domain) and the AIS-based ship domain were similar. Cross-validation of the seafarers’ awareness-based ship domain and the AIS-based ship domain could propose a reference lateral side passing distance for MASS in restricted areas. However, the major axis of the ellipse should be further studied since the domain design method differs between models.

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“…In this regard, a host of scholars have focused on exploring the challenges and risks brought by unmanned and autonomous ships to the shipbuilding industry, maritime environment, maritime supervision, etc., from the perspective of government governance [4][5][6][7][8][9][10], and put forward risk assessment methods and prevention and control strategies at the governance level. Currently, the academic community has mainly delved into collision and cyber security risks of autonomous ships and conducted risk identification and assessment through expert investigation methods [11,12], deep reinforcement machine learning [13], model predictive control (MPC) algorithms [14], evolutionary optimization algorithms [15], field theory [16], causality [17,18], human reliability analysis [19], system theory [20,21], etc. Autonomous collision avoidance navigation systems [13,15], route planning systems [22], cyber risk countermeasures systems [22], and other systems have also been developed.…”

Section: Introductionmentioning

confidence: 99%

Study on the Risk Model of the Intelligent Ship Navigation

Luo

Geng

et al. 2022

Wireless Communications and Mobile Computing

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The perception of risks is a prerequisite for establishing an intelligent ship navigation system based on ship-shore cooperation. According to the research conclusions on the ship-shore collaboration, the technical characteristics of the intelligent ship navigation system, and the analysis of the test results of the ship “ZHI FEI” featured with intelligent navigation, we study and construct the conceptual model and mathematical expression model for risk evolution of the intelligent ship navigation. We further explore the unmanned trend, the general law, and the characteristics of risk evolution based on navigation scenarios and chain effects. The present study provides insight into the risk perception, management, and government governance of intelligent ship navigation and the construction of an intelligent ship navigation system based on ship-shore collaboration.

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A Collision Risk Identification Method for Autonomous Ships Based on Field Theory

Cited by 20 publications

References 41 publications

Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

SASD Modeling Using an ANFIS to Prevent the Collision of MASS in Restricted Areas

Study on the Risk Model of the Intelligent Ship Navigation

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