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Currently, many companies are competing to produce robots that can help humans. Robots can work with high accuracy, efficiency, and low power in the production of goods. Automated Guided Vehicle is one form of robot that is useful in industry. Automated Cars are another form of functional industrial robot that transports with programmable paths and directions automatically. In this paper, we will discuss the navigation system in Automatic Cars based on previously mapped paths. The robot wheel serves to adjust the direction and position of the robot. The automatic movement can be carried out using computer vision. Warehouses that are not neatly arranged cause the navigation system to be less than optimal. The types of controllers used include classical controllers, fuzzy logic, artificial neural networks, and H∞. The purpose of this research is to produce a precise car front view monitoring system using image processing with high enough quality for automatic car navigation. The benefit for science is the development of automatic cars, which so far have not been widely carried out in Indonesia. The results of this study can be useful for the development of methods to build an automatic car system. The benefit for the electronics industry is the availability of control equipment designs to be able to control cars automatically. The effect on society is in the form of future vehicles that can move automatically without a driver. Based on the computer vision system test for Raspberry Pi-based car navigation, it can be ascertained that the car works well and has a small physique, so it looks neat and concise. The movement of the car from the first coordinate point to the next coordinate point is very precise and precise. By installing the L298N motor driver module, it is easier to set the program in python, so that the results sent to the DC motor can be used directly. Using a DC motor that has an excellent performance in terms of torque and speed. [ABSTRACT FROM AUTHOR]