Emerging Computational Methods in Intelligent Marine Vehicles

Dear Colleagues,

Intelligent marine vehicles, such as unmanned/autonomous surface vehicles (USV/ASV) and unmanned/autonomous underwater vehicles (UUV/AUV), have become increasingly popular recently because of their flexibility, versatility and high performance–price ratio in several applications, e.g., ocean exploration, oceanography, military security, and search and rescue missions. Performance in these applications depends highly upon the data sensed from various sensors, such as visible/infrared cameras, LiDAR, radar, global navigation satellite systems (GNSSs), and automatic identification systems (AISs) for USV/ASV, and visible cameras, sonar, inertial navigation systems (INSs), and Doppler velocity logs (DVLs) for UUV/AUV. However, the collected sensed data inevitably suffer from noise and missing data during signal encoding, transmission, and decoding. To guarantee high-quality sensed data, it is necessary to develop advanced computational methods to handle raw data under complex environments. Developing emerging technologies, e.g., data fusion, large language models (LLMs), embodied artificial intelligence (EAI), generative artificial intelligence (GAI), and artificial general intelligence (AGI), have been redefining and expanding traditional application scenarios of marine vehicles. To further improve the capabilities, efficiency, reliability, and safety of USV/ASV and UUV/AUV, emerging computational methods should be considered to handle the intractable problems arising from vehicle perception, decision making, planning, and control.

Potential topics for this Special Issue include, but are not limited to, the following:

• Visual perception enhancement for vision-aided navigation systems;
• Marine object detection, recognition and tracking under complex environments;
• Multi-sensor data fusion for maritime situational awareness;
• Simultaneous localization and mapping (SLAM) for intelligent marine vehicles;
• Behavior and trajectory prediction for intelligent marine vehicles;
• Semantic segmentation for autonomous navigation;
• Unsupervised learning for (multi-task) autonomous navigation;
• Large language models (LLMs) for end-to-end autonomous navigation;
• Embodied intelligence-based perception, decision making, and control for intelligent marine vehicles;
• Generative artificial intelligence (GAI) for testing autonomous navigation systems;
• Artificial general intelligence (AGI) for intelligent marine vehicles;
• Cooperative navigation and control for surface–underwater vehicles.

Prof. Dr. Ryan Wen Liu
Dr. Maohan Liang
Prof. Dr. Kezhong Liu
Guest Editors

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• intelligent marine vehicles
• intelligent transportation
• autonomous navigation
• maritime traffic safety
• artificial intelligence
• computer vision
• multi-sensor data fusion