Prediction Model of Human Error Probability in Autonomous Cargo Ships
4.5 Model validation
This paper could be observed that when the staff on the SCC has to deal with the emergency disposal of autonomous cargo ships in section 4.4. The factors whose posterior probabilities are higher than the prior probability include “negligence when one person monitors multiple ships”, “uncoordinated man-machine interaction”, “situational awareness defect”, “information overload”, “lack of experience in emergency disposal”, “insufficient vigilance” and “insufficient training”, with a combined probability value of greater than 100%. In fact, in the whole system, "negligence when one person monitors multiple ships" and
"uncoordinated man-machine interaction" have the two highest node sensitivities, which significantly impact the occurrence of ship accidents. In other words, these two human error factors are highly likely to cause ship accidents due to the failure in personnel emergency disposal.
Although existing studies focused mainly on human factor identification for autonomous cargo ships, they lacked details about different human error types and their importance in the emergency response disposal by the SCC. For example, Ramos explored human factors in the navigation process of autonomous cargo ships. This study mainly used the event tree analysis to analyse which human error may occur in the ship control and its degree of impact on consequent accidents, based on the progressive order of events. In addition, another study [29] figured out that the most important human errors affecting ship navigation include personnel negligence, information overload, situational awareness defect, skill degradation and insufficient vigilance caused by ignorance. The study emphasized the human factors such as monitoring personnel’s negligence and situational awareness defect, which is consistent with the human error factor ranking presented in this study.
5 DISCUSSION
This study utilized THERP and Bayesian theory to predict human error probabilities in emergency disposal when a ship is controlled remotely by the SCC. The findings manifested that the probability of error by the operator in the SCC during the emergency process was 8.58e-3, which is slightly higher than that of traditional ships. It was observed by a study of existing literature that the researchers are not optimistic about the safety of autonomous cargo ships, because although the human safety is guaranteed when the operators are transferred from the ship to the SCC, the risk index for the ship itself is higher than that of traditional ships. Therefore, there is an urgent need for further research on the safety of autonomous cargo ships.
Based on the human factors sensitivity results in case of emergency disposal of autonomous cargo ships, an analysis of eight risk factors having a high sensitivity score was carried out. The analysis revealed that it was necessary to strictly control the “negligence when one person monitors multiple ships” (A1).
Similarly, the problem of "information overload" (A4) should also be avoided. To manage "uncoordinated human-system interaction" (C4), "situational awareness defect" (C2) and "lack of ship perception" (C3), it is necessary to have realistic simulations and training, while an emergency plan system should be improved to deal with “lack of experience in emergency disposal” (B2). Finally, crew training should be strengthened to avoid "insufficient vigilance" (A2) and “insufficient training” (C5).
In summary, there are several points that the clients should pay attention to when constructing the SCCs and training the operators. These points include: “standardize the number of ships monitored by one person”, continuously “enhance truthfulness of simulated cabins”, strengthening “emergency plan improvement and emergency disposal drills” and mitigating "insufficiency of education and training". These points can provide theoretical basis and reference opinions, thereby reducing human errors in emergency disposal of autonomous cargo ships.
6 CONCLUSION AND FUTURE WORK
This paper analysed the emergency disposal process in the SCC of autonomous cargo ship with the third degree of autonomy. Based on this analysis, the human error probability was divided into three stages of perception-decision-execution, using the human error probability prediction method. Then, the BNs models of the three stages were constructed, followed by calculations of the basic probability of root node based on processing expert opinions using triangular fuzzy numbers. Subsequently, the conditional probability of each intermediate node in the network was also determined, which was used to obtain the human error probability of the entire emergency treatment process. This probability was equal to 8.58e-3.
Finally, the factor importance was ranked based on sensitivity analysis of each human factor. Specifically, the top eight risk factors included “negligence when one person monitors multiple ships” (A1),
“uncoordinated human-system interaction” (C4), “situational awareness defect” (C2), “information overload” (A4), “lack of experience in emergency disposal” (B2), "insufficient vigilance" (A2), "insufficient training" (C5) and "lack of ship perception" (C3). Additionally, Risk Control Options were proposed to provide theoretical suggestions and support for the future construction of SCCs, and staff training.
As the concept of SCC is still in the design stage, the human factor error model for the SCC needs further improvement. The predicted human error probability and conclusions in this paper only serve as a reference for designing a SCC, and risk prevention and control. The human error model can be further studied in future when the SCCs become operational.
ACKNOWLEDGMENTS
The research was supported by the National Key Research and Development Project (2017YFC0804900, 2017YFC0804904), National Key Technologies Research & Development Program (2017YFE0118000). This work has also been supported by the H2020MCSARISE project “RESET -Reliability and Safety Engineering and Technology for large maritime engineering systems” (No.730888).
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International Seminar on Safety and Security of Autonomous Vessels 17 - 18 September 2019, Helsinki