SNS-aware: Multi-modal aware fusion of social-navigation-scene guided vessel trajectory prediction for empowering intelligent transportation system

Journal article, 2027

The evolution of artificial intelligence (AI) has accelerated the development of the maritime Internet of Things (IoT), enabling interconnected maritime ecosystems. Within the maritime IoT, spatio-temporal trajectory data from the automatic identification system (AIS) provide a basis for situational awareness, enabling vessels to perceive navigational risks. However, accurate vessel trajectory prediction is essential for reliable situational awareness. Although deep learning methods achieve stable performance in multi-vessel interaction scenarios, they often neglect semantic information underlying navigational behaviors, limiting generalization in complex environments. Therefore, we propose a social-navigation-scene multi-modal fusion framework (SNS-Aware) for vessel trajectory prediction, comprising four components. The Interaction Encoder captures hierarchical collaborative and conflict-driven interaction patterns. The Behavior Encoder based on temporal convolutional network (TCN) extracts spatio-temporal dynamics, while the Semantic Encoder employs Vision Transformer (ViT) to learn contextualized spatial features from navigation scenes. These multi-modal features are fused in Trajectory Generator, where a conditional variational autoencoder (CVAE) generates diverse realistic trajectories. In addition, binary navigable region masks impose spatial constraints to ensure physically feasible predictions, particularly in narrow waterways. Experimental results on multiple maritime datasets demonstrate that SNS-Aware outperforms baseline methods across various evaluation metrics. These reliable predictions enhance Maritime IoT situational awareness and support intelligent transportation systems.