Intellectual lidar-based object classification for V2V communication technology implementation

Abstract

The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.