Comparative Analysis of Neural Network Models for Autonomous Driving: A Case Study of the NVIDIA model and the DeepDriving Model

Artificial intelligence has significantly advanced technology, bringing once-imaginary concepts like self-driving cars into reality. This research focuses on leveraging AI, computer vision, and neural networks to develop autonomous vehicles capable of lane changes, parking, and navigating various traffic conditions. It compares NVIDIA-based models with deep driving models in simulated environments, highlighting their training methods and drawbacks. NVIDIA-based models, using convolutional neural networks, offer end-to-end learning with large datasets but lack interpretability. Deep driving models decompose tasks, requiring careful feature engineering but providing interpretability. Through simulations, both models are evaluated for accuracy, computational efficiency, and adaptability. This research provides insights into their strengths and limitations, contributing to discussions on improving autonomous driving systems