An unmanned pedestrian track prediction method based on a convolutional neural network

Abstract

The invention discloses an unmanned pedestrian trajectory prediction method based on a convolutional neural network. The method comprises the steps of processing sample data, obtaining an input information sequence, constructing and optimizing the network, and testing and evaluating an optimal model. Real-time video collected by a visual sensor on an unmanned vehicle is segmented into images withframes as units to serve as sample data, target crowds about to pass through zebra stripes in the sample data are divided into three classes, and pedestrian position-proportion information sequence, the pedestrian skeleton information sequence and the motion sequence of the visual sensor,are obtained from samples;, ; the information sequence is input into a convolutional neural network for training to obtain a preliminary prediction model, and after testing and evaluation, finally a prediction track and an action category are output. According to the method, the convolutional neural network isadopted to predict the trajectory of the unmanned pedestrian, so that the probability of pedestrian collision in the road driving process of the unmanned vehicle can be effectively reduced.

Description

technical field [0001] The invention relates to an unmanned pedestrian track prediction method based on a convolutional neural network, which belongs to computer graphics processing technology and artificial intelligence technology. Background technique [0002] With the rise of the artificial intelligence industry, the field of unmanned driving is also developing. Pedestrian detection has received extensive attention. In recent years, scholars at home and abroad have also conducted research on pedestrian prediction, but less research has been done in unmanned driving systems. Pedestrian prediction is to predict the position coordinates of the target at a fixed time in the future through the existing trajectory of the target. [0003] Existing pedestrian trajectory prediction methods include Correlation Filter (CF) correlation filter model, Convolutional Neural Networks (CNN) convolutional neural network method and Social Long Short-Term Memory social long-term short-term me...

AI Technical Summary

Problems solved by technology

[0002] With the rise of the artificial intelligence industry, the field of unmanned driving is also developing continuously. Among them, pedestrian detection has received extensive attention. In recent years, scholars at home and abroad have also conducted research on pedestrian prediction, but less research has been done in unmanned driving systems.

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