2025/03/11 by Jin Wenzhe, Wenzhe, Jin, Tang Haina +3 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Maritime Navigation and Safety #Traffic Prediction and Management Techniques #cs.AI
paper · pdf · doi:10.48550/arxiv.2503.08065
published as Database Systems for Advanced Applications (DASFAA 2025), Lecture Notes in Computer Science, vol. 15987, pp. 571-586, 2026 · This paper has been ACCEPTED as a FULL PAPER at DASFAA 2025
arxiv created 2025/03/11 · openalex publication_date 2025/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28 · arxiv updated 2026/08/04
Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions. Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states. However, existing vessel trajectory prediction methods lack the ability to comprehensively model behavioral multi-modality. To better capture multimodal behavior in interactive scenarios, we propose modeling interactions as dynamic graphs, replacing traditional aggregation-based techniques that rely on vessel states. By leveraging the natural multimodal capabilities of diffusion models, we frame the trajectory prediction task as an inverse process of motion uncertainty diffusion, wherein uncertainties across potential navigational areas are progressively eliminated until the desired trajectories is produced. In summary, we pioneer the integration of Spatio-Temporal Graph (STG) with diffusion models in ship trajectory prediction. Extensive experiments on real Automatic Identification System (AIS) data validate the superiority of our approach.