Ensuring consistent and reliable collision avoidance under perception uncertainty remains a major challenge for maritime autonomous surface ships (MASS). This paper proposes a lightweight and consistency-aware local path planning framework, called consistent Kalman virtual potential field (CKVPF), to improve decision robustness in dynamic and uncertain maritime environments. The framework combines virtual potential field-based planning with Kalman-filtered state estimation and introduces a historical consistency constraint to reduce decision fluctuations over time. CKVPF is implemented on a 45-meter-long MASS platform and tested through full-scale sea trials, with onboard real-time execution achieved using a Raspberry Pi 5. The experiments cover representative encounter scenarios, including head-on, overtaking, crossing and mixed-traffic, where the target ships exhibit unstable motion patterns. Results show that CKVPF maintains consistent decision-making and safely avoids collisions, achieving a 48.9% improvement in navigation path stability compared to baseline methods, while maintaining an average planning time of 177.8 ms. These findings demonstrate the method’s practical applicability and real-time performance for autonomous navigation under real-world uncertainty.

In the field of maritime transportation, precise accurate prediction of ship-speed is paramount for route planning, ship scheduling, and navigational safety. However, the difficulty of the prediction lies in the fact that ship-speed is influenced by numerous complex and variable factors, and traditional speed prediction methods often struggle to attain the desired accuracy in the presence of complex maritime environments and ship characteristics. To address this challenge, this paper aims to develop a robust ship-speed prediction model capable of effectively capturing complex data relationships and improving prediction accuracy and stability. A stacking ensemble learning model is proposed, integrating extra trees (ET), random forest (RF), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and histogram gradient boosting (HGBT) as base learners, with support vector machine (SVM) as the meta-learner to leverage diverse models’ strengths. A standardized workflow for data cleaning, multi-source data fusion, and feature engineering is established. Additionally, the SHapley Additive exPlanation (SHAP) is introduced for model interpretability. Experiments with historical trajectory data from five ships and meteorological-oceanographic data show that the stacking model outperforms single models in prediction accuracy and stability. SHAP analysis reveals that ship course and wave height are key influencing factors, with their impact varying across different navigation scenarios. The proposed model enhances operational efficiency, safety, and decision-making in the maritime industry by providing reliable speed predictions and interpretable insights.

Proactive traffic management is increasingly critical in maritime intelligent transportation systems. Central to this is maritime traffic forecasting, which leverages specific structures and properties of the problem. This study focuses on the traffic dynamics within convergent areas of inland waterways and proposes a method based on data mining followed by prediction using Automatic Identification System (AIS) data. This approach addresses uncertainties in ship voyage destinations and optimizes predictions for temporary stops in inland waterways. AIS data is processed to depict complete ship motion trajectories, grouping them into trajectory sets based on shared origin, destination, and route. These groups help represent maritime traffic patterns using the entrance and exit points of channels and the boundaries of the study area. Additionally, a stop detection model is applied to these trajectories to identify nodes within maritime traffic networks. A decision tree algorithm is then employed to train a classifier for predicting traffic patterns. The method was validated in the convergent area of the Yangtze River and the Hanjiang River, demonstrating effective pattern extraction from inland maritime traffic and high accuracy in predicting single ship trajectories, achieving a 96.7% accuracy rate and 80.9% precision. The findings suggest that the proposed method (1) effectively extracts and predicts traffic patterns, (2) identifies congestion in convergent waters, and (3) supports traffic management strategies.