- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
A Study For Self-Driving Car Analysis | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 24, Issue 4, December 2024, Pages 45-60 PDF (765.55 K) | ||
| Document Type: Case Report | ||
| DOI: 10.33103/uot.ijccce.24.4.4 | ||
| Authors | ||
| Rasha I. Ahmed* 1; Rana M. Hassan* 2; Matheel E. Abdulmunim* | ||
| 1computer science department - university of technology - iraq | ||
| 2computer science ,Technology University, Baghdad, Iraq | ||
| Abstract | ||
| Computer vision has demonstrated itself to be a key component of recent technological advancements. Among computer vision applications, the self-driving automobile. The self-driving cars is one of the most significant developments in the automotive sector. Also Currently, artificial intelligence, especially neural networks, has contributed greatly to the field of automated driving. This paper presents an overview of the use of artificial neural networks for lane detection and demonstrates how this technology has become a key component of vision-controlled autonomous vehicles. Convolutional Neural Networks (CNN) are used for lane detection, which contributes to the car's autonomous operation. Also, the convolutional Neural Networks model gives the vehicle the ability to learn from various road conditions and weather conditions weather rainy, sunny, night or day, granting it the capability of direction prediction so that it can handle any scenario arise which will help the driver to make the right decision and avoid accidents. | ||
| Keywords | ||
| Self-Driving Car; Deep Learning; Deep Neural Networks; Computer Vision; Lane Detection | ||
|
Statistics Article View: 314 PDF Download: 69 |
||