Affiliations: School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu 213164, China | E-mail: wangyeqing2021@163.com
Correspondence:
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Corresponding author: School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu 213164, China. E-mail: wangyeqing2021@163.com.
Abstract: In order to solve the problems of large measurement errors and long time consuming in the measurement of the lateral distance between driverless vehicles, this paper proposes to design a lateral distance measurement technology of driverless vehicles based on machine vision. According to the preview following theory and model basis, the lateral movement process of driverless vehicles is determined, the lateral displacement deviation change rate and heading angle deviation change rate between vehicle and preview point in local coordinates are calculated, and the lateral motion dynamic model of unmanned vehicle is designed. With the help of Yolo algorithm, the boundary frame is drawn up to determine the position of the lateral moving vehicle, and the detection error is corrected with the help of loss function to complete the lateral moving target detection of the unmanned vehicle. The relationship between the machine coordinate system and the world coordinate system is determined. According to the internal and external parameter matrix of the relationship between the lateral distances of the driverless vehicle, the machine vision position is set, and the lateral distance is determined by the pitch angle between the horizontal planes. The experimental results show that the measurement error of this method is always less than 5%. This method has small measurement error, small error between test distance and actual distance, and has certain application performance.
Keywords: Machine vision, driverless vehicles, transverse distance between vehicles, Yolo algorithm, coordinate system
