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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    Title :

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


    Contributors:
    Wei, Kaifeng (author) / Li, Chunming (author) / Li, Rui (author) / Zhao, Yulong (author)


    Publication date :

    2023-09-23


    Size :

    5240873 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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