Research on the Application of Machine Vision Technology in the Autonomous Driving of New Energy Vehicles

This study centers on the application of machine vision technology in the realm of autonomous driving for new energy vehicles. An experimental platform equipped with devices such as cameras was established to amass a substantial amount of road image data across diverse scenarios. Employing object detection algorithms rooted in deep learning, targets like vehicles, pedestrians, and traffic signs within the collected images were identified and localized. During the research process, data augmentation techniques were adopted to expand the dataset and transfer learning was utilized to expedite the convergence of model training, aiming to enhance the algorithm's performance. After numerous experimental tests, this machine vision system can rapidly and accurately recognize various targets, achieving effective perception of the driving environment for new energy vehicles. Moreover, by integrating the Kalman filter algorithm to forecast the motion states of targets, the system's tracking stability for dynamic targets was further enhanced, offering a reliable basis for autonomous driving decisions of new energy vehicles and laying a foundation for promoting the development of this technology in practical applications.