Self-Driving Car Steering Angle Prediction Based On Deep Neural Network An Example Of CarND Udacity Simulator

Abstract

The implementation of self-driving cars will change human lives. Such systems require huge computing power and big datasets. There exists an emulator generating the desired number of images of the vehicle movement easily. We explore the possibility of using obtained images from the emulator for training deep neural networks for prediction of steering angle. This approach allows car to move in automatic mode. We explore various architectures of convolutional neural networks in order to obtain good results with a minimum number of parameters.