MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment
Scene understanding and multisource data fusion are critical challenges in autonomous self-driving systems.In particular, optimizing information fusion strategies for three-dimensional Bird’s Eye View (BEV) scene recognition tasks is crucial for accurate perception and decision-making in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10979852/ |
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| author | Xiubin Cao Yifan Li Hongwei Li |
| author_facet | Xiubin Cao Yifan Li Hongwei Li |
| author_sort | Xiubin Cao |
| collection | DOAJ |
| description | Scene understanding and multisource data fusion are critical challenges in autonomous self-driving systems.In particular, optimizing information fusion strategies for three-dimensional Bird’s Eye View (BEV) scene recognition tasks is crucial for accurate perception and decision-making in dynamic environments. This study proposes a novel architecture that integrates multiscale feature extraction and crossmodal structural alignment to enhance the representation and detection capabilities of BEV features. Specifically, we employ a DCN-based block for visual feature extraction, comprising layer normalization (LN), feedforward networks (FFNs), and the Gaussian Error Linear Unit (GELU) activation function, aligned with the Vision Transformer (ViT) paradigm to improve feature modeling. To fully utilize multiscale information, a dedicated multiscale feature fusion block is introduced to extract expressive scene features within the feature space. Furthermore, we leverage LiDAR to generate LIDAR BEV features and propose a feature alignment block to enhance the complementarity between camera and LiDAR BEV features. The proposed architecture effectively supports precise scene recognition and adaptive decision-making in multi-sensor fusion environments, providing robust perception capabilities for autonomous driving in complex scenarios. |
| format | Article |
| id | doaj-art-a933693874734175985eebb154d6dfda |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a933693874734175985eebb154d6dfda2025-08-20T02:14:45ZengIEEEIEEE Access2169-35362025-01-0113757077571710.1109/ACCESS.2025.356532810979852MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and AlignmentXiubin Cao0https://orcid.org/0009-0002-6438-3058Yifan Li1Hongwei Li2https://orcid.org/0000-0001-6231-4126School of Geo-Science and Technology, Zhengzhou University, Zhengzhou, ChinaInstitute for Geophysics and Meteorology, University of Cologne, Cologne, GermanySchool of Geo-Science and Technology, Zhengzhou University, Zhengzhou, ChinaScene understanding and multisource data fusion are critical challenges in autonomous self-driving systems.In particular, optimizing information fusion strategies for three-dimensional Bird’s Eye View (BEV) scene recognition tasks is crucial for accurate perception and decision-making in dynamic environments. This study proposes a novel architecture that integrates multiscale feature extraction and crossmodal structural alignment to enhance the representation and detection capabilities of BEV features. Specifically, we employ a DCN-based block for visual feature extraction, comprising layer normalization (LN), feedforward networks (FFNs), and the Gaussian Error Linear Unit (GELU) activation function, aligned with the Vision Transformer (ViT) paradigm to improve feature modeling. To fully utilize multiscale information, a dedicated multiscale feature fusion block is introduced to extract expressive scene features within the feature space. Furthermore, we leverage LiDAR to generate LIDAR BEV features and propose a feature alignment block to enhance the complementarity between camera and LiDAR BEV features. The proposed architecture effectively supports precise scene recognition and adaptive decision-making in multi-sensor fusion environments, providing robust perception capabilities for autonomous driving in complex scenarios.https://ieeexplore.ieee.org/document/10979852/Autonomous drivingbird’s eye viewmultiscale feature fusionLiDARfeature alignment |
| spellingShingle | Xiubin Cao Yifan Li Hongwei Li MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment IEEE Access Autonomous driving bird’s eye view multiscale feature fusion LiDAR feature alignment |
| title | MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment |
| title_full | MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment |
| title_fullStr | MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment |
| title_full_unstemmed | MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment |
| title_short | MSFA-BEVNet: Optimization of BEV Scene Recognition Driven by Multiscale Feature Fusion and Alignment |
| title_sort | msfa bevnet optimization of bev scene recognition driven by multiscale feature fusion and alignment |
| topic | Autonomous driving bird’s eye view multiscale feature fusion LiDAR feature alignment |
| url | https://ieeexplore.ieee.org/document/10979852/ |
| work_keys_str_mv | AT xiubincao msfabevnetoptimizationofbevscenerecognitiondrivenbymultiscalefeaturefusionandalignment AT yifanli msfabevnetoptimizationofbevscenerecognitiondrivenbymultiscalefeaturefusionandalignment AT hongweili msfabevnetoptimizationofbevscenerecognitiondrivenbymultiscalefeaturefusionandalignment |