Simulation of Self-driving Car using Deep Learning

The rapid development of Artificial Intelligence has revolutionized the area of autonomous vehicles by incorporating complex models and algorithms. Self-driving cars are always one of the biggest inventions in computer science and robotic intelligence. Highly robust algorithms that facilitate the functioning of these vehicles will reduce many problems associated with driving such as the drunken driver problem. In this paper our aim is to build a Deep Learning model that can drive the car autonomously which can adapt well to the real-time tracks and does not require any manual feature extraction. This research work proposes a computer vision model that learns from video data. It involves image processing, image augmentation, behavioural cloning and convolutional neural network model. The neural network architecture is used to detect path in a video segment, linings of roads, locations of obstacles, and behavioural cloning is used for the model to learn from human actions in the video.