Study on Auto Decision-Making and Its Simulation Control for Vessel Collision Avoidance

Li, Lina; Chen, Guoquan; Guoding, LI

doi:10.1007/978-3-662-46466-3_27

Cited by 2 publications

(1 citation statement)

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“…First, statistical analysis and numerical method are used to support collision avoidance, such as encounter situation and stage discrimination quantitative model [12], with collision parameters. Reference [13] proposed the Personifying Intelligent Decision-making for Vessel Collision Avoidance (PIDVCA) algorithm, which quantified the initial timing of steering rudder and the last time for steering rudder. On the basis of the COLREGS, Woerner et al [14] and Chen et al [6].…”

Section: Collision Avoidance Systemmentioning

confidence: 99%

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Liu

Zhang

et al. 2021

Journal of Advanced Transportation

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Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

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Section: Collision Avoidance Systemmentioning

confidence: 99%

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Liu

Zhang

et al. 2021

Journal of Advanced Transportation

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Research on ontology-based situation understanding and decision-making approach for MASS

Zhang

Ding

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Aiming at the problems of miscellaneous navigation safety information and the difficulty of multi-source heterogeneous information fusion, a novel ontology-based collision avoidance decision making method is proposed in this paper. first of all, the structured ontology model of navigation situation is built on the basis of analysing the hierarchy and interactivity of various scene elements in driving scene and constructing the semantic model of entity class and binary attribute of scene elements, implement effective modelling of navigation scenarios. Secondly, the knowledge base of collision avoidance decision and online reasoning system are constructed based on the semantic expression of typical scene elements to realize the efficient utilization of navigation prior knowledge. Finally, the simulation experiment is carried out for the typical ship encounter scenarios in open water. The results show that the ontology method can effectively improve the cognition ability of Maritime Autonomous Surface Ships (MASS), and meet the requirements for real-time, safety and rationality of collision avoidance decision.

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Study on Auto Decision-Making and Its Simulation Control for Vessel Collision Avoidance

Cited by 2 publications

References 1 publication

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Research on ontology-based situation understanding and decision-making approach for MASS

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