In the field of autonomous driving vehicles, accurately predicting vehicle trajectories is crucial for ensuring safety and enhancing ride comfort. However, predicting the trajectory of a target vehicle is challenging due to complex interaction dynamics with nearby vehicles in different scenarios. To improve prediction accuracy, we introduce a novel spatiotemporal neural network with contrastive learning capabilities. This method delves deep into potential encodings within interaction scenes from both local and global perspectives and incorporates future trajectory features through a Conditional Variational Autoencoder (CVAE). We rigorously evaluate this approach through ablation experiments using the US-101 and I–80 datasets from the National Highway Traffic Safety Administration's Next Generation Simulation (NGSIM) dataset. The research results demonstrate that our method outperforms the current state-of-the-art techniques in terms of performance.


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    Title :

    Spatio- Temporal Neural Network with Contrastive Learning for Vehicle Trajectory Prediction


    Contributors:
    Li, Jiufa (author) / Duan, Shukai (author) / Wang, Lidan (author)


    Publication date :

    2023-10-20


    Size :

    396930 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English