No Cost Autonomous Vehicle Advancements in CARLA through ROS 2021-01-0106
Development of autonomous vehicle technology is expensive and perhaps more complicated than initially thought, as evidenced by the recent rollback of anticipated delivery dates from companies such as Tesla, Waymo, GM, and more. One of the most effective techniques to reduce research and development costs and speed up implementation is rigorous analysis through simulation. In this paper, we present multiple autonomous vehicle perception and control strategies that are rigorously investigated in the user friendly, free, and open-source simulation environment, CARLA. Overall, we successfully formulated potential solutions to the autonomous navigation problem and assessed their advantages and disadvantages in simulation at no cost. First, a lane finding method utilizing polynomial fitting and machine learning is proposed. Then, the waypoint navigation strategy is described, along with route planning. Object detection is then implemented using pre-trained convolutional neural networks. A classic PID control strategy and the Stanley Method were investigated for lateral and longitudinal control of the vehicle. Finally, each of these components are simultaneously applied in the simulation environment using the robot operating system (ROS). As a result, we have achieved successful self-driving simulation. The key takeaway is that the perception and control strategies proposed can be easily transitioned towards physical implementation, through the use of ROS. The overall conclusion is that the CARLA simulation environment is a reliable workbench to test innovative solutions that could become technology enablers for the autonomous vehicle industry.