Intelligent navigation is a crucial component of intelligent ships. This study focuses on the situational awareness of intelligent navigation in inland waterways with high vessel traffic densities and increased collision risks, which demand enhanced vessel situational awareness. To address perception data association issues in situational awareness, particularly in scenarios with winding waterways and multiple vessel encounters, a method based on trajectory characteristics is proposed to determine associations between Automatic Identification System (AIS) and radar objects, facilitating the fusion of heterogeneous data. Firstly, trajectory characteristics like speed, direction, turning rate, acceleration, and trajectory similarity were extracted from ship radar and AIS data to construct labeled trajectory datasets. Subsequently, by employing the Support Vector Machine (SVM) model, we accomplished the discernment of associations among the trajectories of vessels collected through AIS and radar, thereby achieving the association of heterogeneous data. Finally, through a series of experiments, including overtaking, encounters, and multi-target scenarios, this research substantiated the method, achieving an F1 score greater than 0.95. Consequently, this study can furnish robust support for the perception of intelligent vessel navigation in inland waterways and the elevation of maritime safety.

Low quality automatic identification system (AIS) data often mislead analysts to a misunderstanding of ship behavior analysis and to making incorrect navigation risk assessments. It is therefore necessary to accurately understand and judge the quality problems in AIS data before a further analysis of ship behavior. Outliers were filtered in the existing methods of AIS quality analysis based only on mathematical models where AIS data related quality problems are not utilized and there is a lack of visual exploration. Thus, the human brain’s ability cannot be fully utilized to think visually and for reasoning. In this regard, a visual analytics (VA) approach called AIS Data Quality visualization (ADQvis) was designed and implemented here to support evaluations and explorations of AIS data quality. The system interface is overviewed and then the visualization model and corresponding human-computer interaction method are described in detail. Finally, case studies were carried out to demonstrate the effectiveness of our visual analytics approach for AIS quality problems.

The invasion of ship domains stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying the collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validates it through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of the collision risk. Finally, taking the high-risk area for merchant and fishing vessel collisions in the Minjiang River Estuary as an example, this paper conducts an analysis that involves classifying ship collision scenarios, extracts risk data under different collision scenarios, and visually analyzes areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis are verified by the number of accidents that occurred in the most recent three years.

The invasion of ship domain stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validated through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of collision risk. Finally, taking the high risk area of merchant and fishing vessels collision in the Minjiang River Estuary as an example, this paper conduct the analysis involves classifying ship collision scenarios, extracting risk data under different collision scenarios, and visually analyzing areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis is verified by accidents occurred in the recent three years.

With the rapid development of autonomous collision avoidance algorithms on ships, the technical demand for the testing and verification of autonomous collision avoidance algorithms is increasing; however, the current testing of autonomous collision avoidance algorithms is mainly based on the virtual simulation of the computer. To realize the testing and verification of the autonomous collision avoidance algorithm in the real ship scene, a method of virtual reality fusion testing in open water is proposed and real ship testing is carried out. Firstly, an autonomous ship collision avoidance test and evaluation system is established to research the test method of ship encounters in open water. Starting from the convention on the international regulations for preventing collisions at sea (COLREG), the main scenario elements of ship collision avoidance are analyzed. Based on the parametric modeling method of ship collision avoidance scenarios, a standard test scenario library for ship collision avoidance in open waters is established. Then, based on the demand for a ship collision avoidance function test, the evaluation index system of ship collision avoidance is constructed. Subsequently, for the uncertainty of the initial state of the real ship test at sea, the virtual–real space mapping method to realize the correspondence of the standard scenario in the real world is proposed. A standardized testing process to improve testing efficiency is established. Finally, the method of conducting virtual simulation and virtual reality fusion tests for various scenarios are verified, respectively. The test results show that the test method can effectively support the testing of autonomous collision avoidance algorithms for ships in open waters and provide a practical basis for improving the pertinence and practicability of ship collision avoidance testing.