A Survey on Ship Collision Risk Evaluation

Xu, Qingyang; Wang, Weining

doi:10.7307/ptt.v26i6.1386

Cited by 45 publications

(17 citation statements)

References 31 publications

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“…It is a consensus among researchers that clear area around the ship is mainly important for safe navigation [33], Fujii [34] initially introduced the concept that the safety domain is the area where other ships cannot enter for safety, since then several types of safety domain have been studied for different purposes. Various shapes of areas have been developed in safety domain study [35].…”

Section: Detection Rangesmentioning

confidence: 99%

A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Zhou

Zhang

Wang

2021

JMSE

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Large ships are typically with large inertia and longtime delay in motion, in prevailing collision avoidance methods, their maneuverability is generally neglected, there could be a dangerous situation if the system fails to control the ship course as ordered in a timely manner. This paper proposes a coordination system which consists of two algorithms for avoiding risk and then returning to scheduled waypoint. The avoiding risk algorithm are based on VO (velocity obstacle) method, the returning algorithm is derived from LOS (light of sight) guidance. For better performance, the ship model for simulation is a nonlinear Norrbin Model, with the controller improved by CGSA (closed loop gain shaping algorithm) method from traditional PID control, COLREGS (Convention on the International Regulations for Preventing Collisions at Sea) constrains are considered. To test the effectiveness of the proposed system, a series of complex scenarios including Imazu problem are applied.

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Section: Detection Rangesmentioning

confidence: 99%

A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Zhou

Zhang

Wang

2021

JMSE

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“…A variety of methods to assess the collision risk exist in the literature. In their survey on existing methods for collision risk assessment, Xu and Wang [24] categorized the existing methods into three groups: First, the traffic flow theory is a statistical approach to characterize the traffic density of sea areas by analyzing, e.g., the encounter probability or the collision risk. Second, ship domains are used to assess the collision risk.…”

Section: Assessing the Prediction Performancementioning

confidence: 99%

“…Finally, the methods of the Closest Point of Approach (CPA) and time to CPA (tCPA) calculation are mentioned. Here, the vessel movements are approximated as linear vectors, with the help of which, the CPA and tCPA are then calculated, which in turn enables conclusions to be drawn about the collision risk [24].…”

Section: Assessing the Prediction Performancementioning

confidence: 99%

Context-Sensitive Prediction of Vessel Behavior

Steidel

Mentjes

Hahn

2020

JMSE

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Research in the field of maritime anomaly detection and vessel behavior prediction primarily focuses on developing methods for extracting typical vessel movement patterns from historical traffic data. However, contextual information is currently not considered during pattern extraction by existing research. Combining contextual information with historical traffic data has the potential to produce both more accurate traffic patterns and more precise predictions of vessel behavior. This paper investigates the benefit of incorporating contextual information during the extraction of vessel behavior and the prediction of the most probable vessel behavior. A method is presented that combines historical vessel traffic data with information about the course of waterways. Typical behavior patterns are extracted by applying kernel density estimation, which are subsequently used for predicting the most probable vessel behavior. Using this approach, we were able to predict in which area the vessel is most likely to sail, as well as the actual track for a sailing time of 2:35 h. Additional potential applications of our approach can be derived from the results, which, in addition to behavior prediction, can also be used to detect anomalous vessel behavior.

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“…The knowledge of current position (P), navigation (N) and time (T) data of the own ship and ships operating in the same traffic area is one of the key factors for successful collision avoidance [1][2][3][4][5][6][7]. This is reflected by the e-navigation strategy of the International Maritime Organization, which considers the reliable provision of PNT data onboard ships as a major challenge for the further improvement in bridge systems [8,9].…”

Section: Introduction 1pnt Data As Study Subjectmentioning

confidence: 99%

“…In the seventies, the first ship domains were developed to model the space, which a navigator intends to keep free of obstacles to avoid collisions [14,15]. In the following years, work was carried out on improved modelling to enable the use of the ship domain approach for collision avoidance even in narrow channels, port approach areas, and ports, as well as in specific traffic situations [1,2,[16][17][18]22]. Other studies dealt with the application and adaptation of ship domains, either to assess collision risks in specific traffic areas or to support bridge teams in identifying and reducing collision risks [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of PNT Error Limits Using Real World Close Encounters from AIS Data

Engler¹,

Banyś²,

Engler

et al. 2021

JMSE

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Collision avoidance is one of the main tasks on board ships to ensure safety at sea. To comply with this requirement, the direct ship environment, which is often modelled as the ship’s domain, has to be kept free of other vessels and objects. This paper addresses the question to which extent inaccuracies in position (P), navigation (N), and timing (T) data impact the reliability of collision avoidance. Employing a simplified model of the ship domain, the determined error bounds are used to derive requirements for ship-side PNT data provision. For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision. This study assumes that in these situations, erroneous data can lead to an incorrect assessment of the situation with regard to existing collision risks. The size of the error determines whether collisions are detected, spatially incorrectly assigned, or not detected. Therefore, the non-recognition of collision risks ultimately determines the limits of tolerable errors in the PNT data. The results indicate that under certain conditions, the probability of non-recognition of existing collision risks can reach non-negligible values, e.g., more than 1%, even though position accuracies are better than 10 m.

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A Survey on Ship Collision Risk Evaluation

Abstract: ABSTRACT

Cited by 45 publications

References 31 publications

A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Context-Sensitive Prediction of Vessel Behavior

Evaluation of PNT Error Limits Using Real World Close Encounters from AIS Data

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