Accurate and real-time object detection is one of the important functions for autonomous vehicles to accurately perceive the surrounding complex environment. Nevertheless, how to get the accurate size, distance, position, posture and other 3D information of surrounding objects is a classic problem. 3D object detection for autonomous driving has become a popular research field in recent years. Main research progress in this field is reviewed. Firstly, the characteristics of relevant sensors in the surrounding environment of autonomous driving is introduced. Then, the development of object detection from 2D to 3D is introduced and the loss functions is applied for optimization. According to the type of data acquired by the sensor, 3D object detection algorithms is categorized into three types, which are algorithms based on monocular/stereo images, point clouds, image and point cloud fusion. Futhermore, the classic and improved algorithms for each type of 3D object detection are reviewed, analyzed, and compared in detail. Simultaneously, the mainstream autonomous driving datasets and the evaluation criteria of their 3D object detection algorithms are summarized. Extensive experiment results of KITTI and NuScenes datasets are also compared and analyzed, which is widely used inpresent literature, summarizing the difficulties and problems of the existing algorithms. Besides, the opportunities and challenges of 3D object detection in data processing, feature extraction strategy, multi-sensor fusion and data distribution problems are proposed in hope of inspiring more future work.