Deep learning has become the cornerstone of research in autonomous vehicles, enabling perception, decision-making, and control tasks with high accuracy and adaptability. Numerous studies apply convolutional neural networks (CNNs) for object detection, lane recognition, and semantic segmentation of driving environments, while recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are widely used for trajectory prediction, behavior modeling, and sequential decision-making. More advanced frameworks integrate multimodal learning, combining visual, LiDAR, radar, and GPS data to achieve robust sensor fusion. Recent works employ graph neural networks (GNNs) to model interactions among vehicles and pedestrians, reinforcement learning for end-to-end driving policies, and generative adversarial networks (GANs) for simulating realistic driving scenarios and handling data scarcity. These approaches collectively contribute to real-time navigation, obstacle avoidance, and safe decision-making in complex traffic environments, outperforming traditional rule-based or model-driven methods and accelerating the development of reliable self-driving systems.