Designing of an Autonomous System for Electric Vehicle

The creation of an autonomous vehicle has two major goals: to minimize the amount of labor that must be performed manually and to provide the automobile with superior intelligence. In order to turn a standard automobile into an autonomous vehicle that can drive itself around a predetermined map (in this case, a university campus), several modifications need to be made. The outcomes of this project will also contribute to the creation of a more technologically advanced campus atmosphere. Additional responsibilities include taking control of the horn system and being in charge of the headlights while also receiving the response signal from the station. In order to guarantee that everything functions correctly, we utilized components such as the Raspberry Pi, light sensors, motors, Arduino Uno, ultrasonic sensors, buzzers, and cameras. A temporary model is built to ensure that this project is successful right from the start. The training model trains the deep learning approach using images captured by a camera in manual mode, which enables the vehicle to operate in autonomous mode using the multilayered neural network that was taught. The final prototype utilizes image processing methods to track lanes. A few image processing technologies, such as intelligent edge detection, provide autonomous movement capabilities. These algorithms were built and tested on the vehicle. The system operated without a hitch, and the data gathered was within reasonable bounds of expectations. As well as demonstrating how the autonomous system of an electric car works.