Global route planning has garnered global scholarly attention as a crucial technology for ensuring the safe navigation of intelligent ships. The comprehensive influence of time-varying factors such as water depth, prohibited areas, navigational tracks, and traffic separation scheme (TSS) on ship navigation in coastal global route planning has not been fully considered in existing research, and the study of route planning method from the perspective of practical application is still needed. In this paper, a global route planning method based on human-like thinking for coastal sailing scenarios is proposed. Based on the historical route’s information, and taking into full consideration those time-varying factors, an abnormal waypoint detection and correction method is proposed to make the planned route conform to relevant regulations of coastal navigation and the common practices of seafarers as much as possible, and better meet the coastal navigation needs of unmanned ships. Taking the global route planning of “ZHIFEI”, China’s first autonomous navigation container ship, as an example, the validity and reliability of the proposed method are verified. Experimental findings demonstrate the efficacy of the proposed method in global route planning for coastal navigation ships. The method offers a solid theoretical foundation and technical guidance for global route planning research of unmanned ship.

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7.

Local path planning, as an essential technology to ensure intelligent ships’ safe navigation, has attracted the attention of many scholars worldwide. In most existing studies, the impact of COLREGS has received limited consideration, and there is insufficient exploration of the method in complex waters with multiple interfering ships and static obstacles. Therefore, in this paper, a generation method for a time–space overlapping equivalent static obstacle line for ships in multi-ship encounter scenarios where both dynamic and static obstacles coexist is proposed. By dynamically inferring ships’ encounter situations and considering the requirements of COLREGS, the influence of interfering ships and static obstacles on the navigation of the target ship at different times in the near future is represented as static obstacle lines. These lines are then incorporated into the scene that the target ship encountered at the path planning moment. Subsequently, the existing path planning methods were extensively utilized to obtain the local path. Compared with many common path planning methods in random scenarios, the effectiveness and reliability of the method proposed are verified. It has been demonstrated by experimental results that the proposed method can offer a theoretical basis and technical support for the autonomous navigation of unmanned ships.

Risky driving behaviors, such as driving fatigue and distraction have recently received more attention. There is also much research about driving styles, driving emotions, older drivers, drugged driving, DUI (driving under the influence), and DWI (driving while intoxicated). Road hypnosis is a special behavior significantly impacting traffic safety. However, there is little research on this phenomenon. Road hypnosis, as an unconscious state, is can frequently occur while driving, particularly in highly predictable, monotonous, and familiar environments. In this paper, vehicle and virtual driving experiments are designed to collect the biological characteristics including eye movement and bioelectric parameters. Typical scenes in tunnels and highways are used as experimental scenes. LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbor) are employed as the base learners, while SVM (Support Vector Machine) serves as the meta-learner. A road hypnosis identification model is proposed based on ensemble learning, which integrates bioelectric and eye movement characteristics. The proposed model has good identification performance, as seen from the experimental results. In this study, alternative methods and technical support are provided for real-time and accurate identification of road hypnosis.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers’ life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers’ life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.