Accurate shuttle vehicle localization in the indoor environment on a precise map enables the mobile robot to estimate its position and orientation while moving in the indoor environment more efficiently. Trajectory tracking control is one of the fundamental techniques influencing a mobile robot's autonomous driving performance. MATLAB and Linux are high-level computers to equip the Inertial Measurement Unit (IMU), Velodyne VLP-16 channels LiDAR, and Encoder Sensors with the mobile robot platform. The mobile robot controls using a Robot Operating System (ROS) enabled robot, setup parameters for the differential wheels, and visualizes sensor data in a ROS robot visualization tool. In this paper, our model presents a new path following a method that integrates the pure pursuit algorithm and the state flow algorithm using the ROS Simulink model. The path following algorithm that performs autonomous waypoint navigation and obstacle avoidance method successfully localized and tested indoor environment. The accuracy improvement is demonstrated through several experimental results.