Although the long-short term memory (LSTM) network has been widely adopted to predict the vehicle trajectory, the iterative nature of LSTM introduces the accumulative errors. The accumulative errors result in a gradual decrease in the accuracy of trajectory prediction over time. Therefore, how to reduce the accumulative errors of the LSTM is a very critical issue. To solve this problem, we introduce a two-stage attention mechanism with the LSTM Encoder-Decoder model, which uses the spatial attention mechanism and the output attention mechanism to weight the input hidden layer features and the output prediction of the decoder. In this way, the accuracy of trajectory prediction is improved. The cumulative error of the predicted trajectory is significantly reduced. The proposed method is validated on US-101 and I-80 datasets from NGSIM. The simulation results show that the test dataset’s average error at one second and five seconds is reduced from 0.7676 meters and 7.7168 meters to 0.4601 meters and 4.2184 meters, respectively. The average prediction error is reduced by 45.33%.


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    Title :

    Improve the LSTM Trajectory Prediction Accuracy through an Attention Mechanism


    Contributors:
    Zhang, Tong (author) / Wang, Zhiwen (author)


    Publication date :

    2022-06-15


    Size :

    1566097 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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