Development of Autonomous Cars using Raspberry Pi and Google Accelerator for Machine Learning Based Lane Detection

Rayan Hamza Alsisi
jecs@qu.edu.sa (Primary Contact)
1Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia.
Mohamed Uzair
Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
Nader Oudah
Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
Youssef Mabrouk
Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
Omar Alnawajha
Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
Arshad Karimbu Vallappil
Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
Marwan Hadri Azmi
Faculty of Engineering, Department of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Hafiz Abdul Wajid
Faculty of Engineering, Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
PDF

Abstract

Abstract. Autonomous Vehicles (AV) are the smart cars of the future anticipated to be driverless, efficient, and crash-avoiding ideal urban cars. Software complexity, real-time data analytics, verification, and testing are among the more significant challenges in autonomous driving technology. This article presents practical experience and valuable insight into the above-mentioned challenges by developing a lab-scale autonomous car prototype using Rasberry Pi and Google accelerator. The full description of the car, including its technical specifications, the hardware and software design procedures, and the lab-scale circuit for testing, are discussed in detail. The developed prototype is equipped with the machine learning-based lane detection algorithm. The performance of the installed lane detection algorithm is verified by testing the car prototype using the lab scale circuit.

Keywords:

Autonomous Vehicle Artificial Intelligent Machine Learning Computer Vision

Downloads

Download data is not yet available.
Table of Contents
Alsisi, R. H. ., Uzair, M. ., Oudah, N. ., Mabrouk, Y. ., Alnawajha, O. ., Vallappil, A. K. ., Azmi, M. H. ., & Wajid, H. A. . (2024). Development of Autonomous Cars using Raspberry Pi and Google Accelerator for Machine Learning Based Lane Detection. JOURNAL OF ENGINEERING AND COMPUTER SCIENCES, 15(1), 71–94. Retrieved from https://jecs.qu.edu.sa/index.php/jec/article/view/2378
Copyright and license info is not available
Copyright and license info is not available
Author biographies is not available.