In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments.

Collision avoidance is essential for the safe navigation of unmanned surface vehicles (USVs). This paper proposes a multi-ship collision avoidance algorithm that integrates the quaternion ship domain (QSD) and collision risk index (CRI) to assess collision risk and generate optimal avoidance trajectories. The algorithm was used to evaluate target ships (TS) using the CRI values, adapt the ship domain based on QSD, and use the velocity obstacle (VO) method to determine feasible avoidance paths while adhering to the International Regulations for Preventing Collisions at Sea (COLREGs). Through 14 simulation scenarios, including single-ship, two-ship, and multi-ship encounters with unexpected TS maneuvers, the results revealed the ability of the algorithm to maintain safe separation distances in complex maritime traffic. The CRI-based approach allows real-time risk assessment and adaptive timing, while QSD adjusts dynamically based on the ship speed and encounter conditions, minimizing unnecessary maneuvers. This integration of QSD, CRI, and VO ensures COLREGs-compliant navigation, efficient path deviation management, and enhanced safety, contributing to the development of intelligent USV navigation systems.

As the global transportation sector accelerates toward net-zero targets, the rapid deployment of alternative fuels like hydrogen, ammonia, and batteries introduces complex and novel safety challenges. This study systematically maps the intellectual structure of safety and risk research on zero-emission transportation systems to evaluate field maturity and identify critical knowledge gaps. We conducted a comprehensive bibliometric analysis of 151 core publications retrieved from the Web of Science from 2000 to 2025. By integrating quantitative performance analysis with qualitative science mapping techniques, the results identify that the domain is nascent and rapidly expanding, and a distinct inflection in publication occurred in 2020. However, science mapping reveals a fragmented intellectual structure. Among the four identified research clusters, two dominant streams emerge as the primary drivers of the field. The first is a “motor theme” focused on lithium-ion battery reliability and thermal runaway, while the second is a “basic theme” focused on hydrogen dispersion and toxicity risks. The analysis exposes a blind spot regarding the lack of cross-modal research addressing the physical safety interactions between different fuel systems operating in the shared infrastructure. Finally, this study proposes a future research agenda focusing on gathering real-world accident data and using system-theoretic approaches to manage integrated alternative fuel risks.

In swarm control research, the two most important issues are collision avoidance among moving objects and vehicle-to-vehicle communication (V2V communication). Leader-Follower method, the commonly used in swarm control, has problems about these two function. Data loss in communication or a failure of the leader can cause the entire formation to become uncontrollable. To resolve this problem, this paper suggests the variable Leader-Follower method and collision avoidance process based on the Velocity Obstacle method. Instead of fixing a leader, it selects the leader using an allocation algorithm based on the navigation situation. Also, each ship except the leader, in the swarm choose new ideal point using formation matrix that contains the distance from each ship to ideal points. To avoid static and dynamic obstacles, each ships in the swarm make local path using Velocity Obstacle method(VO) with Quadrangle Ship Domain (QSD). If there is no collision risk, they follow the global path; Otherwise, they plans the local path that can avoid collisions with other vessels and obstacles. To validate the algorithm proposed in this paper, navigation simulations are conducted in the simulation environment based on a MMG type manoeuvring model. It is confirmed that the follower can maintain their formation by formation control and appropriate leader was selected based on the current situation by path following control. The collision avoidance simulation with static, dynamic and complex obstacles ensured that the vessels in the swarm can avoid the collision and they can find the optimum formation.