The prototypes of autonomous vessels are expected to come into service within the coming years, but safety concerns remain due to complex traffic and natural conditions (e.g., Yangtze River). However, the response of autonomous vessels to potential accidents is still uncertain. The accident prevention for autonomous vessels is unconvincing due to the lack of objective studies on the causation analysis for maritime accidents. This paper constitutes an attempt to cover the aforementioned gap by studying the potential causations for maritime accidents in the Yangtze River by using a Bayesian-based network training approach. More than two hundred accidents reported between 2013 and 2019 in the Yangtze River are collected. As a result, a Bayesian network (BN) is successfully established to describe the causations among different risk influencing factors. By analysing the BN, this study reveals that the occurrence of maritime accidents (e.g., collision, grounding) can be expected to reduce with the development of autonomous vessels as the crews are removed. However, the extent of the consequences from some accidents (e.g., fire, critical weathers) could be more serious than conventional ones. Therefore, more attention and thoughts are needed to ensure the safe navigation of autonomous vessels in the Yangtze River.

The accurate prediction of vessel trajectory is of crucial importance in order to improve navigational efficiency, optimize routes, enhance the effectiveness of search and rescue operations at sea, and ensure maritime safety. However, the spatial interaction among vessels can have a certain impact on the prediction accuracy of the models. To overcome such a problem in predicting the vessel trajectory, this research proposes a novel hybrid methodology incorporating the graph attention network (GAT) and long short-term memory network (LSTM). The proposed GAT-LSTM model can comprehensively consider spatio-temporal features in the prediction process, which is expected to significantly improve the accuracy and robustness of the trajectory prediction. The Automatic Identification System (AIS) data from the surrounding waters of Xiamen Port is collected and utilized as the empirical case for model validation. The experimental results demonstrate that the GAT-LSTM model outperforms the best baseline model in terms of the reduction on the average displacement error and final displacement error, which are 44.52% and 56.20%, respectively. These improvements will translate into more accurate vessel trajectories, helping to minimize route deviations and improve the accuracy of collision avoidance systems, so that this research can effectively provide support for warning about potential collisions and reducing the risk of maritime accidents.

Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

Newly built offshore wind farms (OWFs) create a collision risk between ships and installations. The paper proposes a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method was tested in offshore waters in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, and ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.