The autonomous navigation of Maritime Autonomous Surface Ships (MASS) highly depend on perception performance. This study utilizes sensors such as shipborne Light Detection and Ranging (LiDAR), Camera, Inertial Measurement Unit (IMU), and Real-Time Kinematic (RTK) to propose a ship perception system based on multimodal data fusion named Shipborne LiDAR-Camera Fusion (SLC-Fusion). In the frontend, a multi-sensor collaborative calibration method based on the iKalibr framework is improved, and adaptive point cloud motion compensation is achieved by combining IMU data. In the backend, the shipborne LiDAR denoising approach based on intensity features is proposed to improve the quality of raw data. Subsequently, the point cloud features after dimensionality reduction are fused with optical images, and an Artificial Intelligence (AI) detection module based on You Only Look Once (YOLO) 11s-seg is used to acquire target semantic and effective pixel indices. Simultaneously, a lightweight port target clustering approach based on local spatial features is proposed to obtain the three-dimensional (3D) states. Finally, the latitude and longitude information of targets are calculated by fusing RTK data. On this basis, this study establishes a perception dataset for port navigation. Real-world experiments demonstrate that SLC-Fusion possesses excellent ship perception performance. The minimum ranging error is 0.07 m (m), with a mean Intersection over Union (mIoU) of 67.0%, an average Mean Absolute Error (MAE) of 0.34 m, and a Root Mean Square Error (RMSE) of 0.47 m. The average processing time for a single frame of images and point clouds is 5.9 ms (ms) and 4.6 ms, respectively.

Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for Maritime Autonomous Surface Ships (MASSs). This paper presents a novel hierarchical motion planning framework that integrates global and local path planning algorithms. For global path planning in static obstacle environments, a hybrid approach combining A*, Bezier curves, and particle swarm optimization (PSO) is proposed to enhance path search efficiency and ensure smooth trajectory optimization. For local path planning and collision avoidance with dynamic vessels, a multi-ship CA algorithm based on velocity prediction potential fields is introduced. A new collision-risk identification model is developed to ensure compliance with COLREGS and enable collaborative CA in complex multi-ship scenarios. The framework also addresses environments with both dynamic and static obstacles through a dynamic path planner that integrates global and local planning. A key innovation is the dynamic CA decision-making mechanism with alteration of course and/or speed (ACS), which provides robust real-time navigation strategies. Validation through simulations, real-ship model tests, and AIS-data analysis demonstrates the framework’s robustness and adaptability for MASS navigation. Compared to existing methods, the proposed approach significantly reduces collision-avoidance path length while maintaining safety, real-time performance, and COLREGS compliance. This work advances autonomous maritime navigation by addressing complex scenarios with a unified framework that balances efficiency, safety, and regulatory adherence.

Understanding ship following behavior in sea ice conditions is essential for safe navigation in the Arctic. In order to unveil the ship following behavior and dominated impact factors, this research studied the ship following navigation characteristics and behavior in the Arctic through a systematic data mining approach. Firstly, a data mining mechanism was developed to extract comprehensive ship following data from the 2022 Arctic AIS dataset, enabling a comprehensive analysis of ship-following behavior. Then, Sea Ice Thickness (SIT) and Sea Ice Concentration (SIC) data are extracted and spatiotemporally linked to the ship following data to reveal the influence of sea ice on the ship following speed and distance within formations. Finally, a quantitative analysis of ship following speeds and distances under varying ice conditions were analyzed. A total of 76 formations are recorded, with formations involving icebreaker being more common, predominantly from January to May in the central Kara Sea and southeast East Siberian Sea. Results revealed that SIT mainly affects ship following speed, while SIC predominantly influences following distance, with significant negative correlations observed in formations with or without an icebreaker. This research elucidates the ship formation characteristics and the influence of sea ice on ship following behavior, providing theoretical guidance for safe formation in the Arctic waters.

Ship trajectory prediction based on Automatic Identification System (AIS) data has attracted increasing interest as it helps prevent collision accidents and eliminate potential navigational conflicts. Therefore, it is necessary and urgent to conduct a systematic analysis of all the prediction methods to help reveal their advantages to ensure safety at sea in different scenarios. It is particularly important and significant within the context of unmanned ships forming a new hybrid maritime traffic together with manned ships in the future. This paper aims to conduct a comparative analysis of the up-to-date ship trajectory prediction algorithms based on machine learning and deep learning methods. To do so, five classical machine learning methods (i.e., Kalman Filter, Gaussian Process Regression, Support Vector Regression, Random Forest, and Back Propagation Network) and eight deep learning methods (i.e., Recurrent Neural Networks, Long Short-Term Memory, Bi-directional Long Short-Term Memory, Gate Recurrent Unit, Bi-directional Gate Recurrent Unit, Sequence to Sequence, Spatio-Temporal Graph Convolutional Network, and Transformer) are thoroughly analysed and compared from the algorithm essence and applications to excavate their features and adaptability for manned and unmanned ships. The findings reveal the characteristics of various prediction methods and provide valuable implications for different stakeholders to guide the best-fit choice of a particular method as the solution under a specific circumstance. It also makes contributions to the extraction of the research difficulties of ship trajectory prediction and the corresponding solutions that are put forward to guide the development of future research.

Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features from extensive historical data. This paper proposes a new learning-based prediction network, improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping, including three views. To effectively enable the training network to capture the temporal and periodic (i.e. a spatial attribute) change characteristics of VTF, the CNN and LSTM are employed to compose spatial and temporal views, respectively. Hence, the original one-dimensional data is transformed into a matrix (hour of the day ✕ day) to adapt the input of the proposed methodology. In practical applications, VTF of multiple adjacent target regions need to be predicted simultaneously, and the changes of VTF in different areas may influence each other. To explore their hidden relationships, the similarity grouping view aims to find the target area that exhibits the most similarity with the VTF change trend of the current research area. Furthermore, similar information is combined with the features generated from the other two views to obtain the prediction results. In summary, the new advantage lies in mining the spatiotemporal attributes of data and fusing the similarity information of adjacent regions. Comparative experiments with eleven other methods on realistic VTF datasets show that the proposed method demonstrates superior prediction accuracy and stability performance.

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships.

Accurate truck arrival prediction is complex but critical for container terminals. A deep learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural Network (FCNN), is proposed to predict daily truck arrivals using fusion technology. The model can efficiently analyze sequence and cross-section data sets. The new feature in the new model lies in that it, for the first time, incorporates the new parameters influencing traffic volumes such as the vessel-related information, arrival weekdays, and weather conditions into the long-time series of truck arrivals. Furthermore, truck arrivals are predicted in three groups based on their movement purposes: pick-up, delivery, and dual. it also contributes to the literature in a sense that the performance of the model is tested using real big data from a world-leading container port in Southern China. The results generate insightful managerial implications for guiding port traffic management in a generic manner. It reveals the relation of export container arrivals with the Container Yard (CY) closing time of a specific vessel. It is demonstrated the proposed model outperforms the currently available methods with an improved accuracy rate of prediction by 23.44% (dual), 32.09% (pick-up), and 26.99% (delivery), respectively. As a result, the model can better reflect reality compared to the existing ones in the literature. It is also evident that the 3-categorized prediction model can significantly help increase prediction accuracy in comparison with the 2-categorized methods used in practice.

Nowadays, sustainability and resilience have become a major consideration that cannot be neglected in urban development. People are starting to consider utilizing the urban infrastructure environment to maintain and improve the functionality and availability of the urban system when unexpected events take place. Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. It has huge impact on urban sustainability and resilience such as overload of the city's infrastructure, and air pollution, etc. This paper presents a survey on the challenges of developing sustainable and resilient transportation networks and the current urban traffic optimisation methods, as a possible solution to address such challenges. It aims to describe and define the state of the art on the research on sustainable and resilient transportation networks in urban development and a taxonomy of different traffic optimisation methods used for avoiding traffic congestion and improve urban traffic management.

This paper improves our previous attempts in which we studied a combination of an evolutionary algorithm (EA) and Monte Carlo simulation (MCS). Results of those studies showed the process of sampling in MCS is very time consuming. This prevents the EA from producing an accurate estimation of the robust solutions within reasonable time. Thus the present work improves the performance of the EA to make it possible to reach high quality solutions in reasonable time, therefore yielding a number of more practical solutions in real cases. Firstly, it proposes a new sampling technique to generate samples that better reflect the worst-case scenarios. This helps the EA to find more robust solutions using smaller sample sizes. Secondly, it proposes a new adaptive sampling technique to adjust the sample size during evolution. Subsequently, to evaluate the proposed algorithm we tested it in a typical environment with shuttle transport tasks: container terminal. Experimental results show that such improvements led to a significantly improved performance of the EA, thus making the proposed algorithm perfectly usable for empirical cases.

This paper presents a novel, efficient fuzzy rule-based Bayesian reasoning (FuRBaR) approach for prioritizing failures in failure mode and effects analysis (FMEA). The technique is specifically intended to deal with some of the drawbacks concerning the use of conventional fuzzy logic (i.e. rule-based) methods inFMEA. In the proposed approach, subjective belief degrees are assigned to the consequent part of the rules to model the incompleteness encountered in establishing the knowledge base. A Bayesian reasoning mechanism is then used to aggregate all relevant rules for assessing and prioritizing potential failure modes. A series of case studies of collision risk between a floating, production, storage, and off loading (FPSO) system and a shuttle tanker caused by technical failure during tandem off loading operation is used to illustrate the application of the proposed model. The reliability of the new approach is tested by using a benchmarking technique (with a well-established fuzzy rule-based evidential reasoning method), and a sensitivity analysis of failure priority values.