Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision
This article explores the application and challenges of computer vision technology in autonomous driving, a critical component for the advancement of this field. Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01013.pdf |
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| _version_ | 1825206578512396288 |
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| author | Chen Xinyi Luo Binbin Xu Ziyue |
| author_facet | Chen Xinyi Luo Binbin Xu Ziyue |
| author_sort | Chen Xinyi |
| collection | DOAJ |
| description | This article explores the application and challenges of computer vision technology in autonomous driving, a critical component for the advancement of this field. Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. The principles, benefits, limitations, and practical challenges of each technology were examined in detail. Thesis findings indicate that CNNs and their variants excel in tasks like object detection and semantic segmentation, significantly enhancing system perception and accuracy. Additionally, multi-sensor fusion technology boosts the reliability and the robustness of autonomous driving systems. However, challenges remain, including high computational demands, environmental perception accuracy, multi-sensor data fusion efficiency, and the high costs associated with implementation. Future research will prioritize developing highly effective deep learning models and optimizing cognitive computing visual systems to ameliorate the efficiency and ensure the safety of autonomous driving. The insights from this study offer valuable references for advancing autonomous driving technology and guide future research directions. |
| format | Article |
| id | doaj-art-9534ef4d24fe4f3f9098f94c2dd849e7 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-9534ef4d24fe4f3f9098f94c2dd849e72025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700101310.1051/itmconf/20257001013itmconf_dai2024_01013Scheme Analysis for Enhancing Autonomous Driving Based on Computer VisionChen Xinyi0Luo Binbin1Xu Ziyue2School of Economics & Management, Beijing Forestry UniversityChengdu Foreign Languages SchoolNanjing Foreign Language SchoolThis article explores the application and challenges of computer vision technology in autonomous driving, a critical component for the advancement of this field. Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. The principles, benefits, limitations, and practical challenges of each technology were examined in detail. Thesis findings indicate that CNNs and their variants excel in tasks like object detection and semantic segmentation, significantly enhancing system perception and accuracy. Additionally, multi-sensor fusion technology boosts the reliability and the robustness of autonomous driving systems. However, challenges remain, including high computational demands, environmental perception accuracy, multi-sensor data fusion efficiency, and the high costs associated with implementation. Future research will prioritize developing highly effective deep learning models and optimizing cognitive computing visual systems to ameliorate the efficiency and ensure the safety of autonomous driving. The insights from this study offer valuable references for advancing autonomous driving technology and guide future research directions.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01013.pdf |
| spellingShingle | Chen Xinyi Luo Binbin Xu Ziyue Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision ITM Web of Conferences |
| title | Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision |
| title_full | Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision |
| title_fullStr | Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision |
| title_full_unstemmed | Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision |
| title_short | Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision |
| title_sort | scheme analysis for enhancing autonomous driving based on computer vision |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01013.pdf |
| work_keys_str_mv | AT chenxinyi schemeanalysisforenhancingautonomousdrivingbasedoncomputervision AT luobinbin schemeanalysisforenhancingautonomousdrivingbasedoncomputervision AT xuziyue schemeanalysisforenhancingautonomousdrivingbasedoncomputervision |