A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles
The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localization, mapping, and navi...
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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/518 |
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| author | Nojood O. Aljehane |
| author_facet | Nojood O. Aljehane |
| author_sort | Nojood O. Aljehane |
| collection | DOAJ |
| description | The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. 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 how deep learning, 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, this study also revealed that the 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 the increased standardization of sensors and real-life testing for model training, and advancements in model compression technologies can optimize the performance of deep learning in AVs. This 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. |
| format | Article |
| id | doaj-art-d387d940a1d44cf390e2588896039e07 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-d387d940a1d44cf390e2588896039e072024-11-26T18:25:52ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-11-01151151810.3390/wevj15110518A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous VehiclesNojood O. Aljehane0College of Computer Science and Information Technology, Tabuk University, Tabuk 71491, Saudi ArabiaThe application of deep learning in autonomous vehicles has surged over the years with advancements in technology. 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 how deep learning, 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, this study also revealed that the 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 the increased standardization of sensors and real-life testing for model training, and advancements in model compression technologies can optimize the performance of deep learning in AVs. This 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.https://www.mdpi.com/2032-6653/15/11/518deep learningautonomous vehiclepivotal rolekey challenges |
| spellingShingle | Nojood O. Aljehane A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles World Electric Vehicle Journal deep learning autonomous vehicle pivotal role key challenges |
| title | A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles |
| title_full | A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles |
| title_fullStr | A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles |
| title_full_unstemmed | A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles |
| title_short | A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles |
| title_sort | study to investigate the role and challenges associated with the use of deep learning in autonomous vehicles |
| topic | deep learning autonomous vehicle pivotal role key challenges |
| url | https://www.mdpi.com/2032-6653/15/11/518 |
| work_keys_str_mv | AT nojoodoaljehane astudytoinvestigatetheroleandchallengesassociatedwiththeuseofdeeplearninginautonomousvehicles AT nojoodoaljehane studytoinvestigatetheroleandchallengesassociatedwiththeuseofdeeplearninginautonomousvehicles |