The invention relates to the technical field of intelligent traffic system vehicle speed prediction, in particular to a clustering and neural network-based vehicle speed prediction method, which comprises the following steps of: calculating Spearman coefficients among characteristics according to vehicle historical driving characteristic data including driving speed, acceleration and front vehicle distance data; determining each feature weight coefficient for clustering, determining the number of clusters according to the contour coefficient index, and clustering the historical data segments by using k-means; and further constructing a speed prediction model based on deep learning for the sample data segments in each cluster. By clustering different speed change modes and establishing a speed prediction model in a targeted manner, the precision of vehicle speed prediction in a complex driving scene is improved.

    本发明涉及智能交通系统车辆速度预测技术领域,具体的说是一种基于聚类和神经网络的车辆速度预测方法,其根据车辆历史驾驶特征数据,包括行驶速度、加速度和前车距离数据,计算特征间斯皮尔曼(Spearman)系数后,确定用于聚类的各特征权重系数,并根据轮廓系数指标确定簇的数量后,采用k‑means对历史数据段进行聚类;进一步针对每一个簇中的样本数据段构建基于深度学习的速度预测模型。通过对不同的速度变化模式进行聚类并针对性的建立速度预测模型,实现对复杂驾驶场景车辆预测速度的精度提升。


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    Titel :

    Vehicle speed prediction method based on clustering and neural network


    Weitere Titelangaben:

    一种基于聚类和神经网络的车辆速度预测方法


    Beteiligte:
    LUO HAOXUAN (Autor:in) / HU XIAO (Autor:in) / HUANG LINYU (Autor:in)

    Erscheinungsdatum :

    2023-12-08


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G07C TIME OR ATTENDANCE REGISTERS , Zeit- oder Anwesenheitskontrollgeräte



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