Learning human’s car-following behavior needs not only well-designed models but also effective training or calibration methods. Comparing with the vast amount of efforts on car-following modeling in literature, training methods are less studied. This research proposes a training method (BPTS - Backpropagation through Simulation) to reduce the long-term error of neural network-based car-following models, with multiple experimental validations. The training method uses a recurrent framework with simulation to generate long-term predictions for generic car-following models, and use gradient backpropagation to reduce accumulative error. The proposed training method can also calibrate other car-following models besides neural network-based models. In experimental validation, our studies yielded more than 30% error reduction in long-term (20 s) prediction for feed-forward Artificial Neural Network (ANN) and Long short-term memory (LSTM) models, and reduces the error on vehicle position by more than 1.0 meters, at the cost of that short-term (0.2 s) prediction error slightly increases. The proposed training method dramatically reduces the long-term prediction error of neural network-based car-following models.


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    Titel :

    Backpropagation through Simulation: A Training Method for Neural Network-based Car-following


    Beteiligte:
    Sun, Ruoyu (Autor:in) / Xu, Donghao (Autor:in) / Zhao, Huijing (Autor:in) / Moze, Mathieu (Autor:in) / Aioun, Francois (Autor:in) / Guillemard, Franck (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    2797638 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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