Accurate and continuous prediction of vehicle trajectories is crucial for the secure deployment of intelligent transportation systems (ITS). Many previous approaches ignore the unique driving behaviors of individuals and have difficulty learning sequential dependencies effectively. However, the driver’s inherent driving pattern will directly impact the subsequent decision. Therefore, we propose a novel framework called Latent Individual Driving Pattern (LIDP), which models historical trajectories emphasizing individual driving patterns. Specifically, we introduce a novel contrastive loss to learn the individual driving pattern. Subsequently, we utilize a representation-augmented module to enhance the expressive capacity of each driving representation. We evaluate the proposed method on two real-world freeway trajectory datasets: US-101 and I-80 in the NGSIM highway dataset, and it achieves promising results, outperforming the recent vehicle trajectory prediction methods.


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    Title :

    LIDP: Contrastive Learning of Latent Individual Driving Pattern for Trajectory Prediction


    Contributors:
    Liu, Chunyu (author) / Yu, Jianjun (author) / Lin, Qiang (author)


    Publication date :

    2024-05-08


    Size :

    1600693 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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