CONTROL SELF-DRIVING CARS USING ADVANCED CONVOLUTIONAL NEURAL NETWORK

Abstract

Convolutional Neural Network (CNN) is one of the most popular Deep Learning network models, capable of recognizing and classifying images with high accuracy, even better than humans. This model has been applied to large image processing systems as Facebook, Google or Amazon... In this paper, we focus on studying some advanced CNN network models (VGG-16), based on VGG-16 architecture, we build new model, by interleaved kernel 3x3, 1x1, increasing number of convolutional blocks, using Exponential Linear Unit (ELU) activation function after each convolution layer, hyperparameter tuning. Apply a new model to predict steering angles in autonomous driving based on image data obtained from Udacity self-driving car simulation. Evaluation, experimentation, and research results show that the steering angle prediction in new model is really effective.