Self-driving cars leverage advanced technologies such as deep learning and convolutional neural networks (CNNs) to process data from various sensors like cameras, LiDAR, and RADAR for autonomous navigation. CNNs are pivotal for tasks such as image classification, object detection, and environment perception, helping cars to recognize and classify road elements and make informed decisions. Companies like Tesla, Waymo, and Nvidia utilize CNN-based architectures like HydraNet and ChauffeurNet to enhance the capabilities of their autonomous vehicles. Decision-making in self-driving cars involves complex algorithms, including reinforcement learning and Markov decision processes, which enable the vehicles to predict and respond to the behaviors of other road users. Despite the technological advancements, challenges remain in achieving full autonomy, as current systems are largely at level-2 out of the desired level-5, requiring further refinement in algorithms and sensor accuracy for better perception and decision-making under varied conditions.