A method for predicting a pedestrian crossing behavior for an intersection includes the following steps: step 1: designing an immediate reward function; step 2: establishing a fully convolutional neural network-long-short term memory network (FCN-LSTM) model to predict a motion reward function; step 3: training the fully convolutional neural network-long-short term memory network (FCN-LSTM) model based on reinforcement learning; and step 4: predicting the pedestrian crossing behavior and performing hazard early-warning. The technical solution does not require establishment of a complex pedestrian movement model or preparation of massive labeled data sets, achieves autonomous learning of pedestrian crossing behavior features at the intersection, predicts their walking, stopping, running and other behaviors, especially predicts the pedestrian crossing behavior when inducing hazards such as pedestrian-vehicle collision and scratch in real time, and performs hazard early-warning on crossing pedestrians and passing vehicles.


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    Title :

    METHOD FOR PREDICTING PEDESTRIAN CROSSING BEHAVIOR FOR INTERSECTION


    Contributors:
    LI XU (author) / HU JINCHAO (author) / HU WEIMING (author)

    Publication date :

    2023-12-21


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06V




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