CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence
arXiv:2605.26524v1 Announce Type: cross Abstract: Maritime intelligent transportation systems (MITS) are essential for ensuring navigation safety and efficiency in busy waterways. However, accurate vessel trajectory prediction remains challenging due to the limitations of single-source data. Automatic identification system (AIS) data is often sparse or unavailable for small vessels, while closed-circuit television (CCTV) data alone cannot fully capture dynamic vessel behavior. To mitigate these challenges, we propose a cross-modal interaction-based vessel trajectory prediction (named CmIVTP) f
The increasing complexity of maritime traffic and the limitations of single-source data necessitate advanced predictive models for navigational safety and efficiency, making cross-modal approaches timely.
Accurate vessel trajectory prediction through multimodal AI directly enhances maritime safety, optimizes Logistics, and provides critical intelligence for naval operations and supply chain resilience.
The shift from single-source to cross-modal data fusion for maritime intelligence offers more robust and reliable predictions, especially for previously underserved or hard-to-track vessels.
- · Maritime logistics companies
- · Defence agencies
- · AI developers (multimodal fusion)
- · Port authorities
- · Traditional maritime surveillance systems
- · Vessels operating without comprehensive tracking
Improved situational awareness and reduced collision risks in busy waterways through more accurate vessel tracking.
Enhanced efficiency in port operations and supply chain management due to better prediction of vessel arrivals and movements.
Potential for fully autonomous maritime navigation systems and more sophisticated naval intelligence capabilities, leveraging real-time, multimodal data streams.
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Read at arXiv cs.AI