The technological advancements of Advanced Driver Assistance Systems (ADAS) sensors enable the ability to achieve autonomous vehicle platooning, increase the capacity of road lanes, and reduce traffic. This article focuses on developing urban autonomous platooning using LiDAR and GPS sensors in a simulation environment. Gazebo simulation is utilized to simulate the sensors, vehicles, and testing environment. Two vehicles are used in this study; a lead vehicle that follows a preplanned trajectory, while the remaining vehicle (follower) uses the LiDAR object detection and tracking information to follow the lead vehicle. The LiDAR object detection is handled in stages: point clouds frame transformation, filtering and down-sampling, ground segmentation, and clustering. The tracking algorithm uses the clustering information to provide position and velocity of the lead vehicle which allows for vehicles platooning. This paper covers the LIDAR object detection and tracking algorithms as well as the autonomous platooning control algorithms. The developed control algorithms were tested in a simulation environment. Test results illustrate that the follower vehicle was able to attain the autonomous platooning based on the LiDAR data.