Accurate detection of vehicle position plays an important role in many intelligent transportation systems, especially vehicle-to-vehicle applications. In this paper, we propose an Extended Kalman Filter (EKF) based method to detect Global Positioning System (GPS) errors for such vehicle-based applications. A machine learning methodology is presented for Kalman filter parameter tuning with application to GPS error correction in vehicle positioning. We also present a model free neural network that is trained on past vehicle GPS trajectories to predict the current vehicle position. Experimental results on real-world data show that the proposed system is effective for detecting and reducing GPS errors. The machine learning algorithm for EKF parameter tuning can be implemented through in-vehicle learning, and the proposed GPS error detection method can be implemented for in-vehicle applications.


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    Titel :

    Accurate vehicle position estimation using a Kalman filter and neural network-based approach


    Beteiligte:
    Baek, Stanley (Autor:in) / Liu, Chang (Autor:in) / Watta, Paul (Autor:in) / Murphey, Yi Lu (Autor:in)


    Erscheinungsdatum :

    01.11.2017


    Format / Umfang :

    1057261 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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