In this project we tried to covert a children’s toy electric car into a self-driving car, using the following techniques:
• Computer Vision • Deep learning methods ie- CNN
 
Self-driving car made in this project is able to navigate the track by making prediction using the trained data set with the help of CNN model. Before feeding the data to CNN model for training, it is preprocessed using computer vision techniques such as Gray Scale, Gaussian blur, canny-edge detection, bitwise AND operator and Hough transform. The preprocessing is done to identify the lane line on track on which car has to move. Initially, tracks are deployed on the ground in order to gather the data in a form of videos using OpenCV with webcam interface. From these videos, images are extracted for classification of the data into four different classes i.e. right, left, forward or stop. Before feeding the data to neural network model, Hough transform is applied using OpenCV for finding the lane line. This data is trained using Convolution Neural Network (CNN) model and a classifier is set which is able to predict in real time whether to move the steering of the car left, right, forward or stop accordingly