The remote control ship is considered to be the most likely implementation of maritime autonomous surface ships (MASS) in the near-term future. With collaborative control from onboard controllers and operators ashore, ships may operate in three navigation control modes (NCMs), manual, autonomous, and remote control, based on different levels of control authority. The scientific selection of the appropriate NCM for MASS under multiple driving modes is crucial for ensuring ship navigation safety and holds significant importance for operators and regulatory authorities overseeing maritime traffic within specific areas. To aid in selecting the proper NCM, this study introduces a risk-based comparison method for determining optimal control modes in specific scenarios. Firstly, safety control paths and processes for MASS under different NCMs are constructed and analyzed using system-theoretic process analysis (STPA). By analyzing unsafe system control actions, key Risk Influencing Factors (RIFs) and their interrelationships are identified. Secondly, a Hidden Markov Model (HMM) process risk assessment model is developed to infer risk performance (hidden state) through measuring RIF states. Cloud modeling with expert judgments is utilized to parameterize the HMM while addressing inherent uncertainty. Lastly, the applicability of the proposed framework was verified through simulation case studies. Typical navigation scenarios of conventional ships in coastal waters were chosen, and real-time data collected by relevant sensors during navigation were used as simulation inputs. Results suggest that in the same scenario, process risks differ among the analyzed NCMs. Traffic complexity, traffic density, and current become the primary factors influencing navigation risks, and it is necessary to select the appropriate NCM based on their real-time changes.

Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation. First, FRAM is used to qualitatively describe the coupling relationship and operation mechanism among the functions of the pilotage operation system. Then, the D–S evidence theory is used to determine the probability distribution of the function output in the specified pilotage scenario after quantitatively expressing the function variability, coupling effect, and the influence of operation conditions through rating scales. Finally, MC simulation is used to calculate the aggregated coupling variability between functions, and the critical couplings and risk resonance paths under different scenarios are identified by setting the threshold and confidence level. The results show that ship collision risk transmission is caused by function resonance in the pilotage system, and the function resonance paths vary with pilotage scenarios. The critical coupling ‘F2-F7(I)’ emerges as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. The hybrid probabilistic risk analytical approach to ship pilotage risk resonance with FRAM can be a useful method for analysing the causative mechanism of ship operational risk.