Special Issue "Autonomous and Remote-Controlled Ship Operations"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editor

Prof. Dr. Salman Nazir
Website
Guest Editor
Faculty of Technology, Natural Sciences and Maritime Sciences, Department of Maritime operations, University of South-Eastern Norway, Raveien 215, 3184 Borre, Norway
Interests: human factors, training methods, virtual reality training simulators, training syllabus, performance indices, performance assessment, performance management, spatial learning, virtual and augmented virtual reality, learning processes, complex socio-technical systems, situation awareness and distributed situation awareness, distributed situation awareness indices, human-autonomy teaming.
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Special Issue Information

Dear colleagues,

Maritime operations have been transforming continually over the past few decades, and recent years have seen an increase in autonomy and automation technologies in the maritime domain. With the advent of autonomous cars in transport, the dream of unmanned cargo carriers in the seaways has experienced a new surge of interest across different stakeholders around the globe. Though the spotlight on unmanned autonomous vessels is evident, practical implementation is still in its infancy. Therefore, humans will remain an important part of maritime systems in varying roles until the aspects of autonomous maritime operations attain maturity. Therefore, it is critical to understand and foresee the potential implications of autonomous technologies on the different segments and actors of the maritime domain. This Special Issue addresses but is not limited to the following areas:

  • Legal and regulatory challanges, e.g., regulatory frameworks of national and international, governmental or nongovernmental stakeholders regarding maritime autonomous ship operations; understanding the liability for actions of autonomous agents; constrains, deficiencies and enablers in the current legal frameworks affecting implementation of maritime automation;
  • Future competence requirements, e.g., relevant future skills of seafarers and maritime personnel; organization, leadership, and subordination in partially or completely remote-operation setup;
  • Maritime education and training (MET) through neopedagogy and training indices in maritime context in the advent of autonomous era, e.g., virtual reality (VR), augmented reality (AR), and the use of other available immersive media, training and bridge resource management adjusted for reduced crew sizes; improved team training; human autonomy team training and improved assessment methods;
  • Practical implementation considerations, e.g., evaluation regarding the compatibility and adaptability of autonomous technologies with human operators; improved evaluation and validation of safety and efficiency of new systems and systems’ elements;
  • Digital infrastructure for autonomous ship operations and management, e.g., shore control centres and connectivity solutions; cybersecurity; maintenance and upgrades; standardization of interfaces and tools;
  • Human performance in autonomous technologies, e.g., human autonomy teaming; psychosocial issues (e.g., trust, complacency, bias); evaluating and understanding human performance in autonomous and remote-controlled ship operations;
  • Enablers for maritime domain to adopt autonomous solutions, e.g., business cases; lessons from other domains; implementation impact modeling.

Prof. Dr. Salman Nazir
Guest Editor

Manuscript Submission Information

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Keywords

  • autonomous ships
  • maritime operations
  • shore control centre
  • seafarer skills
  • remote operations
  • human-centered design
  • Maritime Education and Training

Published Papers (2 papers)

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Research

Open AccessArticle
An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm
Appl. Sci. 2021, 11(2), 799; https://doi.org/10.3390/app11020799 - 15 Jan 2021
Abstract
As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have [...] Read more.
As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port. Full article
(This article belongs to the Special Issue Autonomous and Remote-Controlled Ship Operations)
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Open AccessArticle
Regulatory Requirements on the Competence of Remote Operator in Maritime Autonomous Surface Ship: Situation Awareness, Ship Sense and Goal-Based Gap Analysis
Appl. Sci. 2020, 10(23), 8751; https://doi.org/10.3390/app10238751 - 07 Dec 2020
Abstract
Maritime Autonomous Surface Ship (MASS) has been developed recently, and demonstration projects have been carried out internationally. Considering the full autonomous level is unlikely to be addressed shortly, remote control centre and Remote Operator (RO) will play a vital role in the MASS [...] Read more.
Maritime Autonomous Surface Ship (MASS) has been developed recently, and demonstration projects have been carried out internationally. Considering the full autonomous level is unlikely to be addressed shortly, remote control centre and Remote Operator (RO) will play a vital role in the MASS system. Although competence of watchkeeping at the ship’s bridge is inevitable for RO to avoid ship accidents caused by human errors, international requirements have not been introduced yet. This paper presents a way to develop the regulatory framework on the competence of RO based on the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) by exploring the concept of Situation Awareness (SA). Goal-Based Gap Analysis (GBGA) is constructed based on the human-behaviour model and the required information for SA. A case study through the mini focus group discussion with interviews by a total of three (3) veteran instructors of training ships is conducted utilising the information including the results of previous demonstration projects on the remote control. The results show the relationship between required information and ship sense, shortage of these factors when RO is operating, additional competence and possible regulatory requirements for RO. The findings support the usefulness of GBGA and pave the way to develop a regulatory framework for RO further. Full article
(This article belongs to the Special Issue Autonomous and Remote-Controlled Ship Operations)
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