COLREG and MASS: Analytical review to identify research trends and gaps in the Development of Autonomous Collision Avoidance

Chang, Chia-Hsun; Wijeratne, Isuru Bandara; Kontovas, Christos; Yang, Zaili

doi:10.1016/j.oceaneng.2024.117652

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…An analytical review of relevant literature by Chang et al indicates that the present COLREGs represent a significant linguistic barrier to advances in autonomous navigation [18]. Additionally, the development of artificial intelligence solutions for collision avoidance is facing a lack of quantitative data regarding the COLREGs [19], so quantifying these rules is becoming imperative [20,21].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Expert Knowledge in Describing COLREGs Linguistic Variables

Kristić,

Žuškin

2024

JMSE

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The International Regulations for Preventing Collisions at Sea 1972 (COLREGs) have been the cornerstone of maritime navigation since their introduction. Knowledge and implementation of these rules are paramount in collision avoidance at sea. However, terms found in these rules are sometimes imprecise or fuzzy, as they are written by humans for humans, giving them some freedom in interpretation. The term Very Large Ship used in Rule 7 of the COLREGs is, by its nature, fuzzy. While human navigators understand this term’s meaning, it could be challenging for machines or autonomous ships to understand such an unprecise expression. Fuzzy sets could easily describe unprecise terms used in maritime navigation. A fuzzy set consists of elements with degrees of membership in a set, making them perfect for interpreting some terms where boundaries are unclear. This research was conducted among 220 navigational experts to describe linguistic variables used in maritime regulations. This research consists of an internationally distributed questionnaire. Membership data were collected with the adapted horizontal method, and the results were statistically analyzed, followed by regression analyses to describe the range and shape of membership functions. A conceptual model of the implementation of linguistic variables is presented. The novelty of this study derives from the data collecting, modeling, and quantification of the important but neglected linguistic term Very Large Ship based on a large number of navigational experts. The same quantification method could be easily used for other COLREGs linguistic variables, which could easily lift barriers to advances in intelligent solutions based on fuzzy sets. The obtained quantified fuzzy sets can be used in decision support or control systems used by conventional or autonomous ships in the future.

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Section: Introductionmentioning

confidence: 99%

Quantification of Expert Knowledge in Describing COLREGs Linguistic Variables

Kristić,

Žuškin

2024

JMSE

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“…Though there are several studies on MASS [14][15][16][17][18][19][20][21][22], they are still in the initial stage. Some autonomous inland vessels and ferries are already in operation although these are under strict observation.…”

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confidence: 99%

Systems-Based Safety Analysis for Hydrogen-Driven Autonomous Ships

Sumon,

Kim,

et al. 2024

JMSE

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In the maritime domain, hydrogen fuel cell propulsion and autonomous vessels are two important issues that are yet to be implemented together because of a few challenges. It is obvious that there are several individual safety studies on Maritime Autonomous Surface Ships and hydrogen storage as well as fuel cells based on various risk assessment tools but the combined safety studies that include hydrogen fuel cells on autonomous vessels with recent risk analysis methods are extremely limited. This research chooses the “System-Theoretic Process Analysis” (STPA) method which is a recent method for potential risk identification and mitigation. Both hydrogen and autonomous vessels are analyzed and assessed together with the STPA method. Results are not speculative but rather flexible compared to conventional systems. The study finds a total of 44 unsafe control actions (UCAs) evolved from human and central control unit controllers through STPA. Further, the loss scenarios (LS) are identified that lead to those UCAs so that loss scenarios can be assessed and UCAs can be mitigated for safe operation. The objective of this study is to ensure adequate safety for hydrogen fuel cell propulsion on autonomous vessels.

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