Abstract: The key algorithm in autonomous driving is vehicle recognition based computer vision, which tries to recognize whether vehicles are being located by digital photos or videos. Identifying "blocks," which represent the position of the vehicle in photos or videos, is the fundamental concept behind vehicle detection. Additionally, this work explores 3D stereo-based vehicle identification methods, which emerged from sophisticated planar vehicle detection perception. In terms of the differences between the feature extraction approach and the perceived results, this research concludes by summarizing the vehicle detection algorithms used in recent years. It offers theories for more thorough investigation of the vehicle detection systems. With the rapid advancement of machine learning techniques, this tool is now even more essential for object detection. Compared to manually developed features, machine learning-based picture features are more representative. This review paper focuses on object recognition methods based on machine learning and other deep convolutional neural networks however the traditional object detection techniques will also be briefly reviewed.