Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911901723656192 |
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| author | Zhang, Jingyu Cao, Jin Chang, Jinghao Li, Xinjin Liu, Houze Li, Zhenglin |
| author_facet | Zhang, Jingyu Cao, Jin Chang, Jinghao Li, Xinjin Liu, Houze Li, Zhenglin |
| contents | This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00490 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology Zhang, Jingyu Cao, Jin Chang, Jinghao Li, Xinjin Liu, Houze Li, Zhenglin Computer Vision and Pattern Recognition Artificial Intelligence This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential. |
| title | Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2406.00490 |