With the rapid developments of LiDAR sensors and deep learning technology, researches on 3D object detection for autonomous driving had witnessed remarkable growth. In order to explore the evolution of 3D object detection technology in autonomous driving, the existing deep learning-based 3D object detection methods were summarized. Based on the rely-on sensors, these methods could be divided into three classes: Camera RGB image-based，LiDAR point cloud-based and RGB image-LiDAR point cloud fusion-based 3D object detection. On this basis, the methodology and chronological overview of different kinds of methods were analyzed, and their 3D detection performance and characteristics were compared according to the mean average precision (mAP) metrics. Finally, the principal technical challenges and potential development trends in future autonomous driving 3D object detection researches were summarized and explored.