The perception of risks is a prerequisite for establishing an intelligent ship navigation system based on ship‐shore cooperation. According to the research conclusions on the ship‐shore collaboration, the technical characteristics of the intelligent ship navigation system, and the analysis of the test results of the ship “ZHI FEI” featured with intelligent navigation, we study and construct the conceptual model and mathematical expression model for risk evolution of the intelligent ship navigation. We further explore the unmanned trend, the general law, and the characteristics of risk evolution based on navigation scenarios and chain effects. The present study provides insight into the risk perception, management, and government governance of intelligent ship navigation and the construction of an intelligent ship navigation system based on ship‐shore collaboration.

To address the challenges of sparse point clouds in current MIMO millimeter-wave radar environmental mapping, this paper proposes a dense 3D millimeter-wave radar point cloud environmental mapping algorithm. In the preprocessing phase, a radar SLAM-based approach is introduced to construct local submaps, which replaces the direct use of radar point cloud frames. This not only reduces data dimensionality but also enables the proposed method to handle scenarios involving vehicle motion with varying speeds. Building on this, a 3D-RadarHR cross-modal learning network is proposed, which uses LiDAR as the target output to train the radar submaps, thereby generating a dense millimeter-wave radar point cloud map. Experimental results across multiple scenarios, including outdoor environments and underground tunnels, demonstrate that the proposed method can increase the point cloud density of millimeter-wave radar environmental maps by over 50 times, with a point cloud accuracy better than 0.1 m. Compared to existing algorithms, the proposed method achieves superior environmental map reconstruction performance while maintaining a real-time processing rate of 15 Hz.

Navigational information is of great significance to the safety of maritime navigation. To better guarantee navigator safety and improve navigation efficiency, an applied model of geographic information services (GI services) that consists of an operational architecture, several subsystems and multiple GI services is presented. This work is an e-navigation testbed that follows e-navigation technical architecture and integrates a large amount of navigation-related resources. Real-time, location-based and on-demand digital navigational information can be exchanged and applied in a standardised way. An experiment conducted in the Pearl River Estuary area of the South China Sea showed that application of GI services in e-navigation can supplement the existing methods of exchanging navigational information and better assist navigators in decision making. Furthermore, the proposed model is adaptable and could be easily applied in other areas.

The Universal Hydrographic Data Model is a new generation of marine geographic information data model, referred to as S-100 standard. In addition to supporting Electronic Navigational Charts (ENC) production, S-100 can be used as a Geographic Information System standard to model all maritime objects. Therefore, it is necessary to carry out a comprehensive study on S-100. In this review, we begin with a succinct overview of the history and development of S-100. Then we discussed the problems of S-100 and the corresponding solutions. Finally, some research directions of S-100 are proposed to provide reference for future research and application.

Due to the vast ocean area and limited human and material resources, hydrographic survey must be carried out in a selective and well-planned way. Therefore, scientific planning of hydrographic surveys to ensure the effectiveness of navigational charts has become an urgent issue to be addressed by the hydrographic office of each coastal state. In this study, a reasonable calculation model of hydrographic survey cycle is established, which can be used to make the plan of navigational chart updating. The paper takes 493 navigational charts of Chinese coastal ports and fairways as the research object, analyses the fundamental factors affecting the hydrographic survey cycle and gives them weights, proposes to use the BP neural network to construct the relationship between the cycle and the impact factors, and finally establishes a calculation model of the hydrographic survey cycle. It has been verified that the calculation cycle of the model is effective, and it can provide reference for hydrographic survey planning and chart updating, as well as suggestions for navigation safety.

A great ellipse route (GER), as one of the fundamental routes for ocean voyages, directly influences the actual voyage distance and the complexity of vessel maneuvering through the location and number of its waypoints. Against the backdrop of global warming, the melting of Arctic sea ice has accelerated the opening of the Arctic shipping route. This paper addresses the issue of how to reasonably segment and adopt rhumb line routes to approximate the GER in the special navigational environment of the Arctic. Using historical routes, recommended routes, and geospatial data that have passed through the Arctic shipping lane as constraints, this paper proposes a waypoint optimization model based on an adaptive hybrid particle swarm optimization-genetic algorithm (AHPSOGA). Additionally, by integrating Arctic remote sensing ice condition data and the Polar Operational Limit Assessment Risk Indexing System (POLARIS), a safety assessment model tailored for this route has been developed, enabling the quantification of sea ice risks and dynamic evaluation of segment safety. Experimental results indicate that the proposed waypoint optimization model reduces the number of waypoints and voyage distance compared to recommended routes and conventional shipping industry methods. Furthermore, the AHPSOGA algorithm achieves a 16.41% and 19.19% improvement in convergence speed compared to traditional GA and PSO algorithms, respectively. In terms of computational efficiency, the average runtime is improved by approximately 12.00% and 14.53%, respectively. The risk levels of each segment of the optimized route are comparable to those of the recommended Northeast Passage route. This study provides an effective theoretical foundation and technical support for intelligent planning and decision-making for Arctic shipping routes.