Computer vision serves as the eyes of autonomous vehicles, allowing them to perceive and understand their surroundings through a combination of cameras, sensors, and sophisticated neural networks. Modern autonomous vehicles employ multiple camera systems that capture high-resolution images from different angles, providing a comprehensive view of the environment. These images are processed by deep learning algorithms that perform object detection, classification, and tracking in real-time. Convolutional Neural Networks (CNNs) have been particularly effective for tasks like identifying pedestrians, vehicles, traffic signs, and lane markings. Semantic segmentation algorithms further enhance understanding by classifying each pixel in an image, creating detailed maps of the environment. These technologies, combined with other sensors like LiDAR and radar, enable autonomous vehicles to navigate safely through complex and dynamic environments.