Probabilistic risk analysis for ship-ship collision: State-of-the-art

Chen, Pengfei; Huang, Yamin; Mou, Junmin; Gelder, Pieter van

doi:10.1016/j.ssci.2019.04.014

Cited by 174 publications

(42 citation statements)

References 96 publications

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“…Lin and Yuan [27] proposed a ship collision risk algorithm based on an improved backpropagation neural network. Chen et al [28] proposed a probabilistic risk analysis for ship-ship collision. Li et al [29] studied the ship's trajectory planning based on improved multiobjective algorithm for collision avoidance.…”

Section: Relative Research On the Evaluation Of Ship Collision Riskmentioning

confidence: 99%

Early Warning Method and Model of Inland Ship Collision Risk Based on Coordinated Collision-Avoidance Actions

Cheng

2020

Journal of Advanced Transportation

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To reduce the occurrence of ship collisions, immediate danger, and close-quarters situations in narrow inland waterways, a step-by-step early warning system for ship collision-avoidance actions was developed, along with an early warning method and model of collision risk based on coordinated collision-avoidance actions. This study first analyzed the importance of coordinated collision-avoidance actions in inland waterways, and the process and key components of coordinated collision-avoidance actions were studied. Then, the early warning method of inland ship collision risk based on coordinated collision-avoidance actions was introduced; the effectiveness of the early warning method was comparatively analyzed via experimental observations. A framework of early warning model of inland ship collision risk was created based on the early warning method; a collision risk early warning model for inland ships based on coordinated collision-avoidance actions was proposed according to the relationship between the distance/time to the closest point of approach (DCPA, TCPA), coordination degree of collision-avoidance actions of the two considered ships and collision risk; moreover, the early warning model of inland ship collision risk was further considered for quantitative calculation. Finally, the application of the early warning method and model was demonstrated using a case study. The results indicate that the early warning method of inland ship collision risk based on coordinated collision-avoidance actions could effectively reduce the emergence of close-quarters situations and immediate danger, and the early warning model could quantitatively show the evolution of collision risk of two ships along with the process of coordinated collision-avoidance actions.

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Section: Relative Research On the Evaluation Of Ship Collision Riskmentioning

confidence: 99%

Early Warning Method and Model of Inland Ship Collision Risk Based on Coordinated Collision-Avoidance Actions

Cheng

2020

Journal of Advanced Transportation

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“…The general planned phase reasoning process presented in Algorithm 1 seeks to avoid undesirable and risky situations in the future. The vast literature of collision risk assessment as covered in Section 1.2.2, offers many options to quantify risk [38,15].…”

Section: Density Analysismentioning

confidence: 99%

“…Of these methods, geometric collision probability are methods that work well for ship to ship interactions but difficult to generalize to multi-ship scenarios without arbitrary user inputted weightings [15]. Statistical analysis on accident data including regression or learning not only requires a vast amount of data that are difficult to collect, but might also carries additional computation cost [15]. Due to these concerns, a simplification of what constitutes collision risk is introduced.…”

Section: Density Analysismentioning

confidence: 99%

A planned approach to high collision risk area

Li¹

2020

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This thesis examines the transition of a vessel from the open ocean, where collisions are rare, to a high risk and heavy traffic area such as a Traffic Separation Scheme (TSS). Previous autonomy approaches generally view path planning and collision avoidance as two separate functions, i.e. a vessel will follow the planned path until conditions are met for collision avoidance algorithms to take over. Here an intermediate phase is proposed with the goal of adjusting the time of arrival to a high vessel density area so that the risk of collision is reduced. A general algorithm that calculates maximum future traffic density for all choices in the speed domain is proposed and implemented as a MOOS-IvP behavior. This behavior gives the vessel awareness of future collision risks and aids the collision avoidance process. This new approach improves the safety of the vessel by reducing the number of risky encounters that will likely require the vessel to maneuver for safety.

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“…AIS information was also used for risk identification for vessel contact with fixed offshore platforms [19]. Full reviews on individual ship-ship collision risk analysis and assessment are respectively given in Reference [20] and [21] where a considerable number of research items addressed individual collision risk detection and collision avoidance methods designed and validated using AIS data. In addition to such severe impact of navigation accidents, the recent developments are continuously increasing the traffic capacity and leading to new emerging technologies and vessels characteristics.…”

Section: Introductionmentioning

confidence: 99%

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

Bakdi

Glad

Vanem

et al. 2019

JMSE

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The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

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Probabilistic risk analysis for ship-ship collision: State-of-the-art

Cited by 174 publications

References 96 publications

Early Warning Method and Model of Inland Ship Collision Risk Based on Coordinated Collision-Avoidance Actions

Early Warning Method and Model of Inland Ship Collision Risk Based on Coordinated Collision-Avoidance Actions

A planned approach to high collision risk area

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

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