Abstract Under the framework of Maritime Autonomous Surface Ships (MASS), Level 2 and Level 3 autonomous vessels are expected to play a significant role in future maritime traffic. As maritime traffic density continues to increase due to economic growth, especially in critical waterways, real-time monitoring and effective traffic management are essential for ensuring navigational safety. The Remote Operations Center (ROC) plays a central role in this process, ensuring comprehensive awareness of vessels within the waterway and enabling dynamic responses. Existing ROCs primarily rely on waterway traffic service systems (such as VTS) and departments like the Maritime Safety Administration, combining various tools such as marine radar, AIS data, and shore-based surveillance cameras to provide services. However, existing monitoring technologies face several limitations, such as the latency of AIS data, which makes synchronization with real-time video data challenging, and the limitations of marine radar in obstructed or complex waterways, which affect monitoring effectiveness. Therefore, this paper proposes a real-time vessel monitoring framework based on multi-source data fusion, focusing on using navigation buoys as front-end sensing nodes equipped with cameras, AIS receivers, VHF transceivers, and edge computing devices to achieve local data fusion and analysis. By monitoring the traffic situation in real time at the buoy edge and transmitting relevant information to the ROC, this study establishes a collaborative monitoring and vessel traffic service framework between buoys and the ROC, aiming to enhance monitoring capabilities and service quality in critical waterways.
Ji et al. (Tue,) studied this question.