Copyright © 2025 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{158833,
        author = {Mannem Ravi Teja and Vaishnavi Chandra Peddi Reddy and Irfan Hasan Shaik and Najeema Afrin},
        title = {A driving decision strategy (dds) based on machine learning for an autonomous vehicle},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {781-786},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158833},
        abstract = {The driving methodology of an ongoing independent vehicle not entirely settled by outer variables (people on foot, street conditions, and so on) disregarding the condition of the vehicle's inside. This study proposes "A Driving Decision Strategy (DDS) In light of ML for an Autonomous Vehicle," which considers both outer and inward vehicle components (consumable circumstances, RPM levels, and so on) to decide the best methodology for an independent vehicle. The DDS makes a hereditary calculation to decide an independent vehicle's best driving technique by using cloud-put away sensor information from vehicles. To ensure the DDS's accuracy, this article tested it against MLP and RF neural network models. The DDS identified changes in RPM, speed, controlling point, and path 40 percent more quickly than the MLP and 22 percent more quickly than the RF during the testing. Additionally, its accident rate was approximately 5% lower than that of current vehicle entrances.},
        keywords = {Autonomous vehicles, machine learning, and driving strategy.},
        month = {},
        }