The Human Element and Autonomous Ships

Ahvenjärvi, Sauli

doi:10.12716/1001.10.03.18

Cited by 103 publications

(63 citation statements)

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“…This increase reflects a change of paradigm in human machine systems as machinery systems become increasingly reliable and consequently human operators account for a steady increase in accidents. This increase also shows that higher levels of automation in several industries, such as automotive [94] and maritime [95] have not made the topic less relevant. This is because in most cases automation does not remove the human element completely from the loop, just reallocates it to a different role.…”

Section: Discussion and Future Trendsmentioning

confidence: 99%

Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

Monteiro

Skourup²,

Zhang

2019

IEEE Trans. Human-Mach. Syst.

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This paper provides a brief survey of recent developments on the use of electroencephalogram (EEG) sensors for detecting mental fatigue (MF) in human operators during tasks involving human-machine interaction. This research topic has received much attention since there is a consensus among experts on the increasing relation between human failure and accidents in safety-critical tasks. MF is one of the most influential aspects leading to human failure and the most reliable way to assess it is using operator's physiological data, especially EEG. In the past few decades, hundreds of publications have explored the use of EEG alone or together with other objective and subjective measures for assessing MF, drowsiness, and tiredness in human operators. With recent improvements in data preprocessing, feature extraction, and classification algorithms, the monitoring and mitigation of MF in real time has become a reality. This trend is mainly due to the increasing use of machine learning techniques. This paper provides a comprehensive look at the current state of the art in the field of MF detection using EEG, identifying the currently used technique, algorithms, and methods and possible trends and promising areas for further research. The paper is concluded by suggesting a kernel partial least squares discrete-output linear regression (KPLS-DLR) based model as an all-around good option for an MF assessment system.

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Section: Discussion and Future Trendsmentioning

confidence: 99%

Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

Monteiro

Skourup²,

Zhang

2019

IEEE Trans. Human-Mach. Syst.

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“…e design and rules of each control algorithm and rules for the internal decision logic of the autonomous ship are coded by human so ware engineers. Maintaining situational awareness is also critical to the safe and e ective control of ships [70]. When the operator takes over the ship control subsystem to solve the problem, it means that the possibility of human error is not eliminated, but instead it is transferred to a control center hundreds of miles away [14].…”

Section: Probability Distribution Of Fuzzy Bayesian Networkmentioning

confidence: 99%

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Zhang

Yang

et al. 2019

Journal of Advanced Transportation

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Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations.

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“…Following this, a safety control structure was elaborated. This was achieved by reviewing the available literature pertaining to unmanned merchant vessels, including [9,11,15,16,[22][23][24][25][26].…”

Section: System-theoretic Process Analysis -Stpamentioning

confidence: 99%

“…The main difference between such vision and today's practice is that a human will remain in charge but will not be exposed to weekslong separation from social life and risks present at high seas. Such an arrangement creates certain issues by itself [13,24,36,37].…”

Section: Al-6mentioning

confidence: 99%

A method for uncertainty assessment and communication in safety-driven design - a case study of unmanned merchant vessel

Krzysztof¹,

Jakub²

2018

IJSS

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The Human Element and Autonomous Ships

Cited by 103 publications

References 1 publication

Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

A method for uncertainty assessment and communication in safety-driven design - a case study of unmanned merchant vessel

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