Abstract
Self driving cars commonly known as Autonomous cars. ADAS which stands for Advanced driver assistance system is the most prevailing technologies which uses sensors in the car like radar and cameras to perceive the world around it. Autonomous cars mainly rely on the sensors, processors and software technologies in order to develop the model. In this paper we developed an autonomous car prototype which is capable of driving to maintain lanes, detect objects and recognize zebra crossing and traffic signals. The prototype primarily contains Raspberry pi, USB camera, ultrasonic sensor and driver module. The objectives are achieved by using computer vision techniques, openCV and image processing techniques. The algorithms used in this paper involve feature classification using contour detection, threshold optimization, sobel algorithms, color detection, gaussian filter and edge detection programmed in Python. The proposed system is cheap and can aid future scope for self-driving cars.