Version 1
: Received: 8 July 2024 / Approved: 9 July 2024 / Online: 9 July 2024 (10:50:31 CEST)
How to cite:
Aljehane, N. O. A Study to Investigate the Role and Challenges Associated to the Use of Deep Learning in Autonomous Vehicles. Preprints2024, 2024070741. https://doi.org/10.20944/preprints202407.0741.v1
Aljehane, N. O. A Study to Investigate the Role and Challenges Associated to the Use of Deep Learning in Autonomous Vehicles. Preprints 2024, 2024070741. https://doi.org/10.20944/preprints202407.0741.v1
Aljehane, N. O. A Study to Investigate the Role and Challenges Associated to the Use of Deep Learning in Autonomous Vehicles. Preprints2024, 2024070741. https://doi.org/10.20944/preprints202407.0741.v1
APA Style
Aljehane, N. O. (2024). A Study to Investigate the Role and Challenges Associated to the Use of Deep Learning in Autonomous Vehicles. Preprints. https://doi.org/10.20944/preprints202407.0741.v1
Chicago/Turabian Style
Aljehane, N. O. 2024 "A Study to Investigate the Role and Challenges Associated to the Use of Deep Learning in Autonomous Vehicles" Preprints. https://doi.org/10.20944/preprints202407.0741.v1
Abstract
The application of deep learning in autonomous vehicles has surged over the years with the advances in technologies. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localization, mapping, and navigation. It shows that deep learning, which as a part of machine learning, mimics the human brain's neural networks, enabling advancements in perception, decision-making, localization, mapping, and overall navigation. Techniques like convolutional neural networks are used for image detection and steering control, while deep learning is crucial for path planning, automated parking, and traffic maneuvering. Localization and mapping are essential for AVs' navigation, with deep learning-based object detection mechanisms like Faster R-CNN and YOLO proving effective in real-time obstacle detection. Apart from the roles, the study also revealed that integration of deep learning in AVs faces challenges such as dataset uncertainty, sensor challenges, and model training intricacies. However, these issues can be addressed through increased standardization of sensors, real-life testing for model training, and advancements in model compression technologies can optimize the performance of deep learning in AVs. The study concludes that deep learning plays a crucial role in enhancing the safety and reliability of AV navigation. This study contributes to the ongoing discourse on the optimal integration of deep learning in AVs, aiming to foster their safety, reliability, and societal acceptance.
Keywords
Deep Learning; Autonomous Vehicle; Pivotal Role; Key Challenges
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
