The invention relates to a street-crossing pedestrian trajectory prediction method based on an SFM-LSTM neural network model. The method comprises the following steps: step 1, obtaining motion state information, individual feature information and pedestrian-vehicle interaction scene information of street-crossing pedestrians; 2, performing data preprocessing and data enhancement, and establishing a pedestrian trajectory data set; step 3, establishing and training an LSTM neural network model; 4, obtaining a prediction track of the street crossing pedestrian through the trained LSTM neural network model; 5, performing parameter calibration on the social force model by adopting a maximum likelihood estimation method; 6, correcting the prediction trajectory according to the social force model, and outputting the optimal prediction trajectory of the pedestrian crossing the street; and step 7, broadcasting the optimal prediction trajectory to nearby vehicles to assist intelligent network connection vehicles to make decisions, and compared with the prior art, the method has the advantages of improving the safety of pedestrian crossing, reducing the delay rate of the vehicles, improving the traffic capacity of roads and the like.

    本发明涉及一种基于SFM‑LSTM神经网络模型的过街行人轨迹预测方法,该方法包括以下步骤:步骤1:获取过街行人运动状态信息、个体特征信息和人车交互场景信息;步骤2:进行数据预处理和数据增强,建立行人轨迹数据集;步骤3:建立并训练LSTM神经网络模型;步骤4:通过训练后的LSTM神经网络模型获取过街行人的预测轨迹;步骤5:采用最大似然估计法对社会力模型进行参数标定;步骤6:根据社会力模型对预测轨迹进行修正,并输出过街行人的最优预测轨迹;步骤7:将最优预测轨迹广播至附近的车辆,以协助智能网联车辆进行决策,与现有技术相比,本发明具有提高行人过街的安全性、降低车辆的延误率和提高道路的通行能力等优点。


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Street-crossing pedestrian trajectory prediction method based on SFM-LSTM neural network model


    Weitere Titelangaben:

    一种基于SFM-LSTM神经网络模型的过街行人轨迹预测方法


    Beteiligte:
    ZHANG XI (Autor:in) / YIN CHENGLIANG (Autor:in) / ZHAO BAIXUAN (Autor:in) / CHEN HAO (Autor:in) / LIN YIWEI (Autor:in) / QIN CHAO (Autor:in) / ZHANG YUCHAO (Autor:in) / GAO RUIJIN (Autor:in)

    Erscheinungsdatum :

    2022-05-10


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Crossing-Road Pedestrian Trajectory Prediction via Encoder-Decoder LSTM

    Xue, Peixin / Liu, Jianyi / Chen, Shitao et al. | IEEE | 2019


    Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

    Song, Xiao / Chen, Kai / Li, Xu et al. | IEEE | 2021


    A Posture Features Based Pedestrian Trajectory Prediction with LSTM

    Kao, I-Hsi / Zhou, Xiao / Chen, I-Ming et al. | IEEE | 2021


    Social graph convolutional LSTM for pedestrian trajectory prediction

    Yutao Zhou / Huayi Wu / Hongquan Cheng et al. | DOAJ | 2021

    Freier Zugriff

    Social graph convolutional LSTM for pedestrian trajectory prediction

    Zhou, Yutao / Wu, Huayi / Cheng, Hongquan et al. | Wiley | 2021

    Freier Zugriff