Application of Deep Learning in Autonomous Driving

Autonomous driving technology represents a critical area of innovation within the contemporary technological landscape. Its implications extend beyond the realm of transportation, significantly contributing to economic growth and the boarder process of social development. Mature autonomous driving technologies can significantly lower traffic accident rates, enhancing safety for pedestrians and drivers. Deep learning is a key component of modern AI research, with diverse applications in image processing. Recent advancements in deep learning have enabled its integration with autonomous driving systems. This paper reviews the theoretical and practical applications of deep learning in autonomous driving, presenting an overview of current vehicle applications and a detailed discussion of deep learning concepts and their evolution. It examines key algorithms and models used in this field, including Convolutional Neural Networks, Recurrent Neural Networks, and Deep Reinforcement Learning, summarizing their applications and relevant details. Lastly, the paper addresses future challenges for deep learning in autonomous driving and suggests potential development trends, aiming to provide insights into the current landscape and future directions of deep learning in vehicle technologies.