Research on autonomous vehicle technology has been growing rapidly in recent years. This talk presents our recent works on LiDAR point cloud processing for the perception tasks in autonomous driving, including point cloud classification, semantic segmentation, panoptic segmentation and 3D point cloud depth completion. The unique research contribution is to combine the traditional computer vision algorithms with deep learning models such that the approach can achieve state-of-the-art performance at much lower complexity. Furthermore, those efficient network models are targeted on the GPU and/or FPGA hardware platforms to demonstrate real-time processing for autonomous vehicles. Most of the research results are evaluated using the existing KITTI dataset. The research team also built a full-size autonomous vehicle prototype for data collection and experimentation.