Potential Liability Issues of AI-Based Embedded Software in Maritime Autonomous Surface Ships for Maritime Safety in the Korean Maritime Industry

Kim, Daewon; Lee, Changhee; Park, Sungho; Lim, Sangseop

doi:10.3390/jmse10040498

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…Unmanned ships are essential because they have a variety of commercial [6,7] and military [8,9] applications, and they are very attractive in that they can improve safe navigation [12], especially because most ship accidents arise from human errors [10,11]. According to Wrobel et al, boarding crews have an advantage in responding to a ship accident, but unmanned ships can significantly reduce the risk of an accident occurring in advance [13].…”

Section: Backgroundsmentioning

confidence: 99%

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Kim,

Han,

Lee

et al. 2024

Preprint

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The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASS) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks like real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASS using bibliographic analysis to inform policy and future research directions in this evolving field. We analyze 3,363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. Findings reveal a rapidly growing field dominated by image recognition research. Keywords like "datum," "image," and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "Image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASS and informing future policy and research agendas for the integration of electric propulsion systems in the maritime industry.

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Section: Backgroundsmentioning

confidence: 99%

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Kim,

Han,

Lee

et al. 2024

Preprint

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“…In autonomous shipping and navigation, machine learning extends beyond the realm of real-time vision and decisionmaking to encompass a wide range of essential assignments and capabilities. When designing better control systems that enable autonomous vessels to negotiate challenging marine scenarios with precision and efficiency, machine learning is one area in which it excels [90]- [92]. It is possible to optimize control approaches for autonomous ships by utilizing machine learning algorithms that consider vessel dynamics, environmental factors, and operational limits [93], [94].…”

Section: ) Autonomous Navigation and Shippingmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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“…The identified legal concerns include software product liability and broader legal policy directions. This study proposes practical solutions to balance damage relief and technological progress for maritime safety [11].…”

Section: Previous Research On Maritime Autonomous Surface Ships and B...mentioning

confidence: 99%

“…MASSs are essential because they have a variety of commercial [7] and military [8,9] applications, and they are very attractive in that they can enable safe navigation [10], especially because most ship accidents arise from human errors [11,12]. According to Wrobel et al, boarding crews have an advantage in responding to a ship accident, but MASSs can significantly reduce the risk of an accident occurring in advance [13].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…The rapid increase in research in the field of MASSs is due to a number of factors, including advances in artificial intelligence, robotics, sensor technology, and communication technologies; industrial demands for increased efficiency in the shipping industry; changes in the safety and regulatory environment; increasing investment; and the need for technological approaches to minimize environmental impacts [11]. These technical and industrial motivations drive research on MASSs, which is reflected in the rapid increase in the number of research papers.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Kim,

Han,

Lee

et al. 2024

Electronics

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The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

show abstract

Potential Liability Issues of AI-Based Embedded Software in Maritime Autonomous Surface Ships for Maritime Safety in the Korean Maritime Industry

Cited by 9 publications

References 36 publications

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

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