A Review of Machine Learning Techniques Utilised in Self-Driving Cars

Authors

DOI:

https://doi.org/10.52866/ijcsm.%202024.05.01.015

Keywords:

Autonomous driving vehicles, Vehicle detection, Pedestrian detection, Traffic sign detection, Convolutional Neural Network(CNN)

Abstract

Science and technology researchers are currently focused on the creation of self-driving cars. This can have a profound effect on social and economic progress; self- driving vehicles can help reduce auto accidents dramatically and enhance the quality of life of people the world over.  Self-driving cars have had a tremendous increase in popularity in the recent past because of artificial intelligence development. However, there is a lot of research work  to be done to manufacture fully-automated cars because a self-driving carshas tto be able to sense its environment and operate without human involvement. A human passenger is not required to take control of the vehicle at any time, nor are they required to be present in the vehicle at all.

Currently, self-driving cars are still at level 3 and are not allowed ply the roads due to many challenges which usually cause blurred images,  including irregular roads, weather factors (rain and fog).

This paper is a review study on self-driving cars, and will be examining the obstacles that self-driving cars face, as well as how they might overcome them. The paper will provide the researchers with pieces of information about self-driving cars, the challenges they face, the recent methods used to overcome these challenges, and the advantage, disadvantage, and accuracy of these methods. The paper aims to encourage researchers to work on solving the problems that inhibit the evolution of self-driving vehicles.

Downloads

Download data is not yet available.

Downloads

Published

2024-02-06

How to Cite

[1]
Z. S. Dhaif and Nidhal K. El Abbadi, “A Review of Machine Learning Techniques Utilised in Self-Driving Cars”, Iraqi Journal For Computer Science and Mathematics, vol. 5, no. 1, pp. 205–219, Feb. 2024.
CITATION
DOI: 10.52866/ijcsm. 2024.05.01.015
Published: 2024-02-06

Issue

Section

Articles