CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation
Abstract The environmental perception of intelligent connected vehicles is a new perception paradigm centered around roadside sensing devices and 5G wireless communication. This approach expands the perception range of onboard sensors from localized observation to a global view, significantly enhanc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08482-5 |
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| author | Caizhen He Hai Wang Tong Luo Shunyao Zhang Long Chen Yingfeng Cai |
| author_facet | Caizhen He Hai Wang Tong Luo Shunyao Zhang Long Chen Yingfeng Cai |
| author_sort | Caizhen He |
| collection | DOAJ |
| description | Abstract The environmental perception of intelligent connected vehicles is a new perception paradigm centered around roadside sensing devices and 5G wireless communication. This approach expands the perception range of onboard sensors from localized observation to a global view, significantly enhancing the accuracy of vehicle perception. Traditional perception algorithms often face issues in real-world datasets, such as blurriness of target features from the vehicle’s perspective and loss of specific information when integrating vehicle and infrastructure features. To address these issues, we propose a Cooperative Perception network for Discrepancy feature fusion through Knowledge Distillation, named CPD-KD. The contributions of this work are threefold: First, we introduce a Sparse convolution-based Knowledge Distillation network (SKD) to alleviate blurring of single-view point cloud features by incorporating prior knowledge from fused viewpoint point cloud features. Second, we design a Discrepancy Feature Attention Fusion module to enhance Cooperative efficiency of differential information between vehicles and infrastructures. Finally, we validate the CPD-KD algorithm on the real-world dataset DAIR-V2X and the simulated dataset V2X-Set. Experimental results show that the proposed CPD-KD algorithm effectively enhances the accuracy of vehicular and infrastructural Cooperative perception. |
| format | Article |
| id | doaj-art-a63ba863cff7431d9e04ed5fa88ee145 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a63ba863cff7431d9e04ed5fa88ee1452025-08-20T03:42:22ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-08482-5CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillationCaizhen He0Hai Wang1Tong Luo2Shunyao Zhang3Long Chen4Yingfeng Cai5School of Automotive and Traffic Engineering, Jiangsu UniversitySchool of Automotive and Traffic Engineering, Jiangsu UniversitySchool of Automobile and Traffic Engineering, Jiangsu University of TechnologyAutomotive Engineering Research Institute, Jiangsu UniversityAutomotive Engineering Research Institute, Jiangsu UniversityAutomotive Engineering Research Institute, Jiangsu UniversityAbstract The environmental perception of intelligent connected vehicles is a new perception paradigm centered around roadside sensing devices and 5G wireless communication. This approach expands the perception range of onboard sensors from localized observation to a global view, significantly enhancing the accuracy of vehicle perception. Traditional perception algorithms often face issues in real-world datasets, such as blurriness of target features from the vehicle’s perspective and loss of specific information when integrating vehicle and infrastructure features. To address these issues, we propose a Cooperative Perception network for Discrepancy feature fusion through Knowledge Distillation, named CPD-KD. The contributions of this work are threefold: First, we introduce a Sparse convolution-based Knowledge Distillation network (SKD) to alleviate blurring of single-view point cloud features by incorporating prior knowledge from fused viewpoint point cloud features. Second, we design a Discrepancy Feature Attention Fusion module to enhance Cooperative efficiency of differential information between vehicles and infrastructures. Finally, we validate the CPD-KD algorithm on the real-world dataset DAIR-V2X and the simulated dataset V2X-Set. Experimental results show that the proposed CPD-KD algorithm effectively enhances the accuracy of vehicular and infrastructural Cooperative perception.https://doi.org/10.1038/s41598-025-08482-5LiDAR sensorsKnowledge distillationV2X perception |
| spellingShingle | Caizhen He Hai Wang Tong Luo Shunyao Zhang Long Chen Yingfeng Cai CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation Scientific Reports LiDAR sensors Knowledge distillation V2X perception |
| title | CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation |
| title_full | CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation |
| title_fullStr | CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation |
| title_full_unstemmed | CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation |
| title_short | CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation |
| title_sort | cpd kd a cooperative perception network for discrepancy feature fusion through knowledge distillation |
| topic | LiDAR sensors Knowledge distillation V2X perception |
| url | https://doi.org/10.1038/s41598-025-08482-5 |
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