The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident's probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers. 

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship.

Exploring seafarers’ emotional responses to emergencies: An empirical study using a shiphandling simulator

Influence of sea ice on ship routes and speed along the Arctic Northeast Passage

Towards an analysis framework for operational risk coupling mode: a case from MASS navigating in restricted waters

The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is 8.259×10−2 and the collision probability is 4.129×10−3. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased. 

Advanced Human Reliability Analysis Approach for Ship Convoy Operations via a Model of IDAC and DBN: A Case from Ice-Covered Waters

The study of Arctic navigation safety is becoming increasingly important due to the rise in commercial and strategic significance of Arctic shipping routes. To explore the state of risk assessment methodologies in the context of Arctic navigation safety, this paper conducts a comprehensive bibliometric analysis of 149 articles, along with an in-depth review of 22 articles. The analysis reveals that despite a surge in research focus since 2015, particularly regarding collision risks involving ship-to-ship and ship-to-ice encounters in the Northeast passage, substantial gaps exist in applying these models in practice. The study highlights that equipment failures are largely neglected in academic discussions, and the Northwest Passage is under-researched despite its operational challenges, such as difficulties in predicting ice conditions due to wind and currents from the North American continent, smaller navigation windows, and so forth. The most applied risk assessment models are identified for various decision-making contexts, acknowledging their methodological limitations. The paper concludes with a call for decision-oriented modelling techniques and the bridging of academic research with practical application. It recommends incorporation of overlooked risk factors, such as equipment failures, human error, and region-specific navigational challenges, to develop more robust and applicable risk assessment models for various decision scenarios. 

Review of risk assessment for navigational safety and supported decisions in arctic waters

Hybrid early-warning framework for unsafe crew acts detection and prediction

The dynamic autonomy resulting from control mode transitions in the Maritime Autonomous Surface Ships (MASS) poses a significant risk to its navigation safety. While Systems-Theoretic Process Analysis (STPA) has proven effective in the hazard identification of autonomous ships, the safety challenges inherent in the dynamic autonomy of MASS with seafarers onboard controlled by three possible controllers have not been addressed much. This study proposes a three-phase framework that combines STPA, state machines and Sequentially Timed Events Plotting (STEP) diagram aiming at identifying failure scenarios in the transition process. Findings from a case study highlight the need for such a framework to understand the triggering events for mode transition, the transition process, how transition failure may occur, and refine safety constraints to ensure smooth and safe transitions. The results lay the groundwork for future research into test case generation methods that focus on these identified transition failure scenarios, intending to ensure safe and secure navigation for the progressive introduction of MASS. 

A framework to identify failure scenarios in the control mode transition process for autonomous ships with dynamic autonomy

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Review of risk assessment for navigational safety and supported decisions in arctic waters

Hybrid early-warning framework for unsafe crew acts detection and prediction

A framework to identify failure scenarios in the control mode transition process for autonomous ships with dynamic autonomy