Vehicle Detection Using Point Cloud and 3D LIDAR Sensor to Draw 3D Bounding Box

In recent times, Autonomous driving functionalities is being developed by car manufacturers and is revolutionizing the automotive industries. Hybrid cars are prepared with a wide range of sensors such as ultrasound, LiDAR, camera, and radar. The results of these sensors are integrated in order to avoid collisions. To achieve accurate results a high structured point cloud surroundings can be used to estimate the scale and position. A point cloud is a set of Data points used to represent the 3D dimension in X, Y, Z direction. Point cloud divides data points into clusters that are processed in a pipeline. These clusters are collected to create a training set for object detection. In this paper, the cluster of vehicle objects and other objects are extracted and a supervised neural network is trained with the extracted objects for the binary classification of the objects representing vehicles or other objects. By learning global features and local features the vehicle objects represented in the point cloud are detected. These detected objects are fitted with a 3D bounding box to represent as a car object.