This research explores deep neural networks (VGG16, AlexNet,and GoogleNet) for object classification and detection in autonomousvehicles. We use public datasets like Udacity and Penn-Fudan. VGG16achieves near-perfect accuracy, AlexNet performs well, and GoogleNetexcels but with longer processing times. Balancing accuracy and efficiencyis crucial for real-time computer vision in autonomous driving.Future work may focus on transfer learning with VGG16 or dataset augmentation.This study provides insights for deep learning models to enhanceindependent vehicle safety and efficiency.