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Self-Driving robotic car utilizing image processing and machine learning

The major goal of this paper is to build and represent a prototype of a fully autonomous car that employs computer vision to detect lanes and traffic signs without human intervention using limited computing capacity. The project contains an embedded system represented by a Raspberry Pi 3 which serves as the image processing and machine learning unit. This method requires a stream of images as input for the computer vision using OpenCV2 library with C++ programming language along with Haar Cascade Classifier for the detection of traffic signs. The Raspberry Pi will send binary signals to the Arduino UNO which is responsible for merging those signals with the ones from the ultrasonic sensor and producing new signals which are sent to the motor driver to control the direction and speed of the dc motors. The system was able to detect the lane and respond to changes in lane direction, as well as to detect traffic signs and give appropriate responses.