In the advanced period the vehicles will be robotic to allow humans to drive freely. In the automotive field, a different point of view has been considered which makes the vehicle robotic. In this project, thinking about the distinctive features and cost, a small-scale mechanical model of a three-wheeled vehicle that will follow the route and evade deterrents has been outlined. Autonomous cars are a creative innovation that could prove to be the next huge development in individual transportation. This report begins by describing the scene and major players in the self-driving car landscape. The current capabilities, limitations, and possibilities of key enhancement advances are examined along with a discussion of the effects of such advances on society and the earth. Most of the effects, including the reduction of movement and stopping the blocking of autonomous versatility for poor people, the increase of safety and preservation of vitality, and the decrease of pollution, can be noteworthy when the Self-sufficient vehicles become normal and reasonable for average people. Raspberry pi is the central processor of our autonomous car. The camera module captures several images, and several image management methods are used on these images to realize artificial intelligence. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get Original Essay Traffic light and signal detection helps autonomous locomotives in industries by providing the necessary commands to facilitate the flexible manufacturing system. Autonomous locomotives in industry are used for material handling. Automatic signal recognition guides autonomous locomotives to move in the correct direction. The path tracking of autonomous locomotives is described by the steering control system. The main objective is to design a method for steering control system for autonomous locomotives by rearranging signs and traffic lights. The system provides an efficient locomotive system in a flexible manufacturing environment. Image processing techniques are used to regulate road signs and to command certain actions. The input to the system is video data that is continuously captured by the webcam interfaced to the Raspberry Pi through the open CV platform where the Raspbian operating system is used. The images are pre-processed with different image processing techniques, for example hsv color space model techniques are used for traffic light detection, for signal detection with respect to hsv color space model and algorithm is used outline. Signals are detected based on the region of interest (ROI). The ROI is detected based on characteristics such as the geometric shape and color of the object in the image containing traffic signs. The steering control system uses DC motors and motor drives for operation. Gurjashan Singh Pannu et al, proposed a “Design and Implementation of an Autonomous Car Using Raspberry Pi”, the summary is as follows, Elodyne 64-beam laser produces detailed 3D guidance of the surrounding environment. The car then combines the laser measurements with high-resolution world maps creating different types of data models that allow it to drive itself while maintaining a strategic distance from deterrents and obeying traffic laws. The components used to design Google Car are four radar sensors mounted on the front and rear bumpers 1.