Assessment of Ship Collision Risk in Coastal Waters by Fuzzy Comprehensive Evaluation

Park, Jin-wan; Jeong, Jung-Sik

doi:10.5391/jkiis.2020.30.4.325

Cited by 4 publications

(10 citation statements)

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“…However, it is difficult to determine the existence of collision risk using only quantitative assessments. Therefore, the collision risk detection algorithm [19][20][21] is applied to automatically inform ASVs of the existence of collision risk. This algorithm detects collision risk by applying "time to safe distance", which indicates the time it takes for the distance between two vessels to reach a safe distance.…”

Section: Methodsmentioning

confidence: 99%

Improved Collision Avoidance Method for Autonomous Surface Vessels Based on Model Predictive Control Using Particle Swarm Optimization

Park¹

2021

IJFIS

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Most collision accidents are caused by human errors in an encounter situation between vessels. Autonomous operations have received much interest among researchers to reduce human errors resulting from improper vessel maneuvering actions. In this paper, an intelligent collision avoidance method that considers not only the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) but also complex encounter situations with multiple obstacle vessels was proposed for autonomous surface vessels (ASVs). The results were verified by comparing the performance with that of existing methods. To take timely maneuvering actions to prevent the collision of vessels involved in an encounter, fuzzy comprehensive evaluation, and model predictive control using particle swarm were applied to path planning. As a result, ASVs can take proper action to avoid collisions in compliance with COLREGs based on the proposed method. The improved collision avoidance method exhibits better performance than existing methods.

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Section: Methodsmentioning

confidence: 99%

Improved Collision Avoidance Method for Autonomous Surface Vessels Based on Model Predictive Control Using Particle Swarm Optimization

Park¹

2021

IJFIS

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“…In the Cartesian coordinate system, it is assumed that its origin is the position of the own vessel, (X, Y) denotes the coordinates corresponding to the relative position of the target vessel, V o denotes the true velocity of the own vessel, V t denotes the true velocity of the target vessel, V r denotes the relative velocity of the target vessel with respect to the own vessel, and V rx , V ry denote the components of V r . Based on this assumption, we can obtain the DCPA, TCPA, D, and K as follows [20,21]:…”

Section: Preprocessing Ais Datamentioning

confidence: 99%

“…According to this process, we surveyed the experts of navigation and constructed the set of pairwise comparison matrices to calculate the weight values of each target factor. As a result, the following weight vector was obtained [21]:…”

Section: Cri Calculationmentioning

confidence: 99%

“…The greater the membership value, the greater the collision risk of encountered vessels. The membership value of each target factor could be obtained through the following membership functions as applied in [3,12,17,21,23]: DCPA is the most important factor that is considered as a spatial indicator of collision risk, and the smaller the value of DCPA, the greater the collision risk. The membership function of DCPA is as follows:…”

Section: Cri Calculationmentioning

confidence: 99%

“…In order to validate the models developed by SVM and RVM, as indicated in [20], the algorithm for the detection of collision risk was applied [21]. The algorithm detects collision risk stably by introducing the time to safe distance (TDs), which is the time required to reach the safety distance (Ds) between encountering vessels.…”

Section: Model Validationmentioning

confidence: 99%

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An Estimation of Ship Collision Risk Based on Relevance Vector Machine

Park

Jeong

2021

JMSE

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According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

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Assessment of Collision Risk in Vessel Traffic Service Areas using Fuzzy Comprehensive Evaluation

Park¹,

Jeong

2022

2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced

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Assessment of Ship Collision Risk in Coastal Waters by Fuzzy Comprehensive Evaluation

Cited by 4 publications

References 0 publications

Improved Collision Avoidance Method for Autonomous Surface Vessels Based on Model Predictive Control Using Particle Swarm Optimization

Improved Collision Avoidance Method for Autonomous Surface Vessels Based on Model Predictive Control Using Particle Swarm Optimization

An Estimation of Ship Collision Risk Based on Relevance Vector Machine

Assessment of Collision Risk in Vessel Traffic Service Areas using Fuzzy Comprehensive Evaluation

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