Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{167187,
        author = {Dr. Poovarasan Selvaraj and Ajithkumar. R},
        title = {APPLICATION BASED SELF-DRIVING CAR SIMULATOR USING MODIFIED CNN NVIDIA MODEL},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {526-531},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167187},
        abstract = {Self-driving car system using NVIDIA’s Convolutional Neural Network (CNN) model, which maps raw pixel data from a single front-facing camera directly to steering commands. Leveraging deep learning, we trained the model on a diverse dataset encompassing city streets, highways, and off-road terrains. The simulation showcases the model's ability to autonomously navigate complex driving conditions without traditional components like lane detection, path planning, or control algorithms. Instead, the model learns to interpret and respond to road features and driving scenarios solely from human steering inputs during training. Our findings highlight the benefits of an end-to-end learning approach, where the CNN optimizes the entire driving task integrally, achieving robust performance across various driving contexts. This method potentially enhances efficiency and effectiveness over traditional autonomous driving systems, demonstrating the feasibility of streamlined, deep learning-based solutions for self-driving technology. },
        keywords = {DAVE-2 System, CNN, CNN Model, Autonomous Land Vehicle in a Neural Network (ALVINN) system.},
        month = {August},
        }