Conditional temporal GAN for intent-aware vessel trajectory prediction in the precautionary area

Abstract

Accurate vessel trajectory prediction is crucial for ensuring maritime traffic safety and efficiency, particularly in precaution areas characterized by multi-waterway branch merging and frequent traffic conflicts. However, predicting trajectories in these areas poses significant challenges due to the uncertain future intents of different directional branches resulting in diverse motion patterns and multiple possible paths. To address this issue and minimize the prediction error, we propose a conditional temporal generative adversarial network (CTGAN). Specifically, in this method, the trajectory generator is developed to capture the inherently dynamic of ship motions and outputs the future trajectory proposals, while the intent classifier is designed to evaluate whether the trajectory proposals are consistent with the hidden intention. With the adversarial training strategy, the trajectory generator and intent classifier form a closed loop, feedback informative signals to each other that enables generating the intent-constrained trajectory. In addition, a mixed adversarial loss function was designed to capture the spatial–temporal dependencies among the vessel motions for producing consistent trajectories that complied with plausible ship dynamics. Experiments on extensive naturalistic vessel trajectory data demonstrate that compared with the baseline methods, the proposed model achieves comparable or better prediction performance.