Car Auomation Simulator Using Machine Learning

Abstract

Recent advancement in computation power of computer has enabled research in self – driving vehicle. A self-driving vehicle is autonomous in nature and do not need any driver for making decisions on the movement of vehicle. Self-driving vehicle reads the data by capturing images from the four sides of the car and use GPS technology to find path between given source and destination. Once path is fixed then there is need for mathematical model to make decisions on the movement of vehicle. There are two ways to make decisions. One way is to read the images and detect various objects in the image and then taking movement decisions but this method requires training on huge set of labeled images even to detect few objects. Second way is to use CNN to extract features from a given image and training a model to take decisions on movement of vehicle. Training of model using neural networks requires considerably smaller image dataset than required with object detection. Convolutional Neural Network consists of layers of neurons. Successive layers of neural network are used to extract features from the image. A simulator for self-driving car is required to simulate real world traffic conditions and then check the trained model in this environment. This simulation is necessary before model can be implemented on actual vehicle to prevent loss of life and loss of vehicle due to model inaccuracies. Real world traffic can be simulated with the help of mission games having good graphics like Grand Theft Auto. Proposed project is to develop a simulator for self-driving vehicle using Grand Theft Auto for road and traffic simulation. This project has four phases. First phase involves reading input to the game and output from the game. Second phase involves collecting data from the game. Third phase is to train a mathematical model using CNN. Finally, fourth phase is to check trained model and detecting other objects for driving a vehicle in game.