Exploring autonomous driving methods, in simulation or video games

In this thesis, we explore the application of computer vision techniques for autonomous driving in a video game setting. We focus on the task of predicting the optimal motion the vehicle in the game using only visual information from the screen. To this end, we propose a dataset specifically designed for this task, and evaluate a variety of state-of-the-art classification models on it. Our results show that these models can successfully predict the motion of other vehicles with high accuracy, and are able to run in real-time on the game engine. Furthermore, we also present an analysis of the results and discuss the limitations of our approach. As a future work, we propose to explore other computer vision techniques such as object detection and semantic segmentation to improve the performance of the models and to incorporate more information from the game environment.