CONVOLUTIONAL NEURAL NETWORKS APPLIED TO TRAFFIC SIGN DETECTION IN GRAND THEFT AUTO V

Abstract

In recent years, more and more companies have continued to join the quest of developing fully autonomously driven vehicles. With a relatively recent research report suggesting that by 2030, the technologies of autonomous driving will have developed into a global industry worth $87 billion US dollars, it is no wonder so many companies are investing so heavily now in creating such technologies. Many issues and obstacles need to be addressed and resolved though, before fully autonomously driven vehicles can be sold to consumers and used on public streets. Perhaps the most fundamental issues are those of giving the autonomously driven vehicles the ability to actually drive accurately, safely, and in accordance with all street laws. One specific obstacle is for the vehicle to recognize road signs just as a human would normally, such as detecting stop signs, traffic lights, speed limit signs, and warning signs. The focus of this study is on improving upon current detection methods by boosting accuracy (less false detections, less missed detections) and reducing image analysis time. This is done by attempting a more difficult single-step approach to traffic sign detection, as opposed to the traditional relatively easier two-step approach described in Chapter 2. This study first attempts to develop a reliable traffic sign detector by constructing, training, and tuning various Convolutional Neural Networks. Images for training are obtained both from real world public datasets and images from the game of Grand Theft Auto V. It then attempts to explore the advantages of using a virtual environment (in this case a video game) to train detectors for autonomous driving. It concludes there are distinctive, measurable advantages to training such detectors in a virtual environment. Investments in constructing virtual environments for training and testing autonomously driven vehicles should be seriously considered.