PreCLN: Pretrained-based contrastive learning network for vehicle trajectory prediction

Trajectory prediction of vehicles is of great importance to various smart city applications ranging from transportation scheduling, vehicle navigation, to location-based advertisements. Existing methods all focus on modeling spatiotemporal relations with explicit contextual semantics from labeled trajectory data, and rarely consider the effective use of large amounts of available unlabeled trajectory data with the assistance of contrastive learning and pre-training techniques. To this end, we develop a novel Pretrained-based Contrastive Learning Network (PreCLN) for vehicle trajectory prediction. Specifically, we propose a dual-view trajectory contrastive learning framework to achieve self-supervised pre-training. A Transformer-based trajectory encoder is designed to effectively capture the long-term spatiotemporal dependencies in trajectories to embed input trajectories into fixed-length representation vectors. Moreover, three auxiliary pre-training tasks, i.e., trajectory imputation, trajectory destination prediction, and trajectory-user linking, are used to assist the training of PreCLN with the dual-view trajectory contrastive learning framework. After pre-training, the result trajectory encoder is used to generate trajectory representations for future trajectory prediction. Extensive experiments on two real-world large-scale trajectory datasets demonstrate the significant superiority of PreCLN against state-of-the-art trajectory prediction baselines in terms of all evaluation metrics.