{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:31:51Z","timestamp":1773693111404,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"research and development of intelligent vehicle key technologies and industrialization projects based on new-energy vehicles","award":["TC210H02S"],"award-info":[{"award-number":["TC210H02S"]}]},{"name":"research and development of intelligent vehicle key technologies and industrialization projects based on new-energy vehicles","award":["20220301012GX"],"award-info":[{"award-number":["20220301012GX"]}]},{"name":"quantitative development and measurement technology research of expected functional safety based on vehicle\u2013cloud collaboration","award":["TC210H02S"],"award-info":[{"award-number":["TC210H02S"]}]},{"name":"quantitative development and measurement technology research of expected functional safety based on vehicle\u2013cloud collaboration","award":["20220301012GX"],"award-info":[{"award-number":["20220301012GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from the camera. For image feature extraction, the ResNet50 + FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and voxel fusion methods. After information fusion, the Coordinate and SimAM attention mechanisms extract fusion features at a deep level. The algorithm\u2019s performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm; the proposed algorithm improves the mAP value by 7.9% in the BEV view and 7.8% in the 3D view at IOU = 0.5 (cars) and IOU = 0.25 (pedestrians and cyclists). At IOU = 0.7 (cars) and IOU = 0.5 (pedestrians and cyclists), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.<\/jats:p>","DOI":"10.3390\/s23218732","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T07:22:15Z","timestamp":1698304935000},"page":"8732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multiattention Mechanism 3D Object Detection Algorithm Based on RGB and LiDAR Fusion for Intelligent Driving"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3674-4018","authenticated-orcid":false,"given":"Xiucai","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Lei","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Junyi","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Baoyun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Yuhai","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Yuanle","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. 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