Self-driving cars, as one of the new technologies in the field of transportation, are rapidly developing and advancing. Among the key factors in improving the performance of these cars is the use of artificial intelligence and neural networks. This article examines recent developments in this area and introduces new solutions for better control and management of autonomous vehicles.
In this article, we first introduce the key concepts of artificial intelligence and neural networks in the field of autonomous vehicles. Then, it examines the types of algorithms and artificial intelligence methods used to control self-driving cars, including amplified algorithms, deep neural networks, and reinforcement learning. The main innovation of this article is to provide new approaches to improving the efficiency and safety of autonomous vehicles. Taking advantage of recent advances in artificial intelligence, this paper provides solutions to improve decision-making accuracy, identify obstacles, and increase safety in autonomous car systems.
Finally, the potentials and challenges of these new approaches in the field of self-driving cars are examined and suggestions for future research in this field are made.This article helps scientists and researchers in the field of automotive and artificial intelligence to have a deeper understanding of the development and optimization of autonomous vehicles.
Bibtex:
@inproceedings{joshan2024greater,
title={Unlocking the future: Significant Advances in Artificial Intelligence and Neural Networks in the Control and Management of Self-Driving Cars},
author={Joshan, Alireza},
booktitle={journal of New Interdisciplinary Researches in Basic and Technical Sciences and Engineering},
volume={4},
number={1},
year={2024}
}