Improving the accuracy of vehicle speed prediction is the key to developing a Tracked vehicle power distribution Strategy. This paper proposes a vehicle speed prediction method based on driving condition identification. The K-means clustering algorithm is used offline to classify the conditions into stationary conditions and quickly-changing conditions, and the current driving condition is judged in real time during the driving process of the vehicle. The Markov-chain prediction method is adopted in stationary conditions while Long Short-Term Memory (LSTM) prediction method is adopted in quickly-changing conditions. Under the premise of maintaining a certain prediction accuracy, it can save calculation time and improve real-time performance.


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    Titel :

    The Vehicle Speed Prediction Method of Tracked Vehicles Based on Driving Condition Identification


    Beteiligte:
    Wei, Kaifeng (Autor:in) / Li, Chunming (Autor:in) / Li, Rui (Autor:in) / Zhao, Yulong (Autor:in)


    Erscheinungsdatum :

    23.09.2023


    Format / Umfang :

    5240873 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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