Application of Deep Learning to Generate Auto Player Mode in Car Based Game

This paper sets out a framework for constructing deep learning models for autonomous driving in Forza Horizon 5. By playing a game for few hours, we focus on a merging model of AlexNet and Deep Reinforcement Learning known as AlexNet-DRL. The data are useful for research and include ambient characteristics, model choices, and vehicle telemetry. Time efficiency, collision rates, and perception accuracy are all evaluated as well. Comparatively, the performance analysis of autonomous driving in object recognition, collision avoidance, and real-time decision making can be done in comparison to AlexNet-DRL. Although AlexNet-DRL shows promise, it only has 92.5% perceptual accuracy, and humans have 95%. The human player had a collision rate of 1.5%, while the model had 3.4%. Results contribute to the development of models on DRL for autonomous navigation that can be more advanced than humans.