• Shruti Godghate
• P. G. Chilveri
• S. S. Gaikwad
• Harshvardhan shende

Vehicles are the most preferable means of transportation for both work and household living in today's society. Automobiles are used for a wide range of purposes, resulting in a rising number of road accidents. By building a self-driving car prototype and installing machine learning algorithms such as CNN and SVM, we will be able to determine which machine learning method has the best accuracy in this suggested system. This will require the use of a processor on which the car will operate. The Raspberry Pi is the CPU we're using. Images collected by the camera will be the system's input database. We will detect the obstacles and the path's road lines using the provided database. This data will then be given to the processor as an input, where it will be processed using machine learning techniques such as CNN and SVM, and the output will be sent to the self-driving car's motor driver and motor. Then, based on the results of our proposed theory, we can calculate the model's accuracy as well as monitor obstructions and lane detection using some parameters. Then we'll have to repeat the operation with a different algorithm, which we'll have to implement the same way we did before. As a result, we can determine which method (Algorithm) has the better accuracy, and this will be the project's final conclusion.

Self driving car, Machine learning algorithms, safety measures, object detection.

"Self-Driving Car Navigation, Implementation and Comparison using Machine Learning Algorithm and Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.522-526, August-2022, Available :http://www.jetir.org/papers/JETIRFP06093.pdf

"Self-Driving Car Navigation, Implementation and Comparison using Machine Learning Algorithm and Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp522-526, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06093.pdf