A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS

Nguyen, Manh-Cuong; Zhang, Shufang

doi:10.3390/a11120204

Cited by 23 publications

(12 citation statements)

References 30 publications

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“…Furthermore, it is generally not easy to compare path planning and collision avoidance algorithms for ASVs due to the variety of constraints and objectives that exist. One example is the use of regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs) [13]: whereas some algorithms successfully generate paths for avoiding obstacles whilst simultaneously obeying several COLREG Rules [e.g., [14][15][16][17][18], others fully or partially ignore these regulations [e.g., 11,19,[20][21][22]. For adoption in the future, fully autonomous surface vessels must comply with all the rules of COLREGs.…”

Section: Properties Of Algorithmsmentioning

confidence: 99%

“…Adaptive-BFOA [21] The algorithm is applicable in real time The path does not get trapped in local optimum The rules of the road can be properly taken into account The algorithm is very robust and reliable, given the diversity of marine traffic environment It is assumed that target ships do not change their courses and speeds Multi-objective PSO [48] Compliance with COLREGs…”

Section: Limitationsmentioning

confidence: 99%

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Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

et al. 2021

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Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.

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Section: Properties Of Algorithmsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

et al. 2021

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“…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%

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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“…where W and U represent the weight and membership vector of the target factors, respectively. The membership value for each target factor can be calculated using the membership function [19,20,22,24,25], which can be stated for DCP A as follows:…”

Section: Collision Risk Assessmentmentioning

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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A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS

Cited by 23 publications

References 30 publications

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

An Estimation of Ship Collision Risk Based on Relevance Vector Machine

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

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