Maritime Transportation

Research 


Deep Learning Framework for Vessel Trajectory Prediction Using Auxiliary Tasks and Convolutional Networks

The accurate prediction of vessel trajectories plays a pivotal role in various maritime applications, including route planning, collision avoidance, and maritime traffic management. With the exponential growth in vessel traffic and the increasing complexity of maritime operations, there is a pressing need for reliable and efficient methods to forecast vessel movements. Traditional statistical and machine-learning approaches have limitations in capturing the complex spatial-temporal patterns of vessel movements. Deep learning techniques have emerged as a promising solution due to their ability to handle large-scale datasets and capture nonlinear relationships. This paper proposes a novel deep learning-based vessel trajectory prediction framework for AIS data using Auxiliary tasks and Convolutional encoders (AIS-ACNet). The model leverages Automatic Identification System (AIS) data, including geographical positions, and vessel dynamics such as Speed Over Ground (SOG), and Course Over Ground (COG), for trajectory prediction. The AIS-ACNet employs parallel convolutional encoder networks with feature fusion layers. The model is trained with a learning objective that includes auxiliary tasks such as SOG and COG predictions. This framework enhances the model's representative power of vessel trajectory data leading to a better understanding of vessel dynamics and higher trajectory prediction performance. The proposed framework is evaluated on real-world data from the Port of Houston, Texas, USA, and compared to existing models through extensive experiments and ablation studies. The results demonstrate the effectiveness and superiority of AIS-ACNet in accurately predicting vessel trajectories.

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Sample AIS Dataset

Prediction of all trajectories within a day