{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:43:07Z","timestamp":1785404587347,"version":"3.56.0"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Science and Technology Program","award":["AA24263010"],"award-info":[{"award-number":["AA24263010"]}]},{"name":"Guangxi Science and Technology Program","award":["AB23026120"],"award-info":[{"award-number":["AB23026120"]}]},{"name":"Guangxi Science and Technology Program","award":["AA24263006"],"award-info":[{"award-number":["AA24263006"]}]},{"name":"Guangxi Science and Technology Program","award":["AA24206043"],"award-info":[{"award-number":["AA24206043"]}]},{"name":"Guangxi Science and Technology Program","award":["AD25069103"],"award-info":[{"award-number":["AD25069103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U23A20280"],"award-info":[{"award-number":["U23A20280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62471153"],"award-info":[{"award-number":["62471153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U25A20397"],"award-info":[{"award-number":["U25A20397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["U23A20280"],"award-info":[{"award-number":["U23A20280"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62471153"],"award-info":[{"award-number":["62471153"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["U25A20397"],"award-info":[{"award-number":["U25A20397"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Nanning Scientific Research and Technology Development Program","award":["20231029"],"award-info":[{"award-number":["20231029"]}]},{"name":"Nanning Scientific Research and Technology Development Program","award":["20231011"],"award-info":[{"award-number":["20231011"]}]},{"DOI":"10.13039\/100012547","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2024GXNSFBA010265"],"award-info":[{"award-number":["2024GXNSFBA010265"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automotive millimeter-wave radar produces sparse point clouds with Doppler velocity and radar cross-section (RCS), but graph detectors typically use a shared representation for semantic prediction and box regression despite their different propagation requirements. We propose multi-GSO spectral filtering (MGSF), a residual module that filters radar features over geometry-, Doppler-, and RCS-defined graph shift operators and fuses diffusion and residual components with a node-adaptive gate. MGSF-TD applies full multi-GSO refinement to semantic prediction and geometry-only refinement to box regression. On the complete RadarScenes validation set, MGSF-TD improves the official RadarGNN checkpoint from 60.19 to 60.59 mAP and from 74.06 to 75.10 mean foreground F1 (FG-F1). Across three MGSF-TD training seeds, the FG-F1 margin under RCS noise increases from +1.15 at 3 dBsm to +2.38 at 20 dBsm; seed-42 full-validation mAP margins are +0.24, +1.07, and +1.82. Controls show that geometry-only diffusion explains part of the gain and the RCS operator contributes most clearly at low-to-moderate noise, whereas a parameter-matched widened baseline matches or exceeds MGSF-TD under severe RCS and Doppler corruption. Cross-sensor diagnostics reproduce the Doppler failure trend but not the severity-dependent RCS gain. MGSF-TD therefore offers a balanced, physically interpretable operating point rather than a universal robustness gain.<\/jats:p>","DOI":"10.3390\/s26154714","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T08:15:56Z","timestamp":1785140156000},"page":"4714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-Guided Multi-GSO Spectral Filtering for Degradation-Aware Automotive Radar Point-Cloud Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4771-9587","authenticated-orcid":false,"given":"Xiuping","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4227-2499","authenticated-orcid":false,"given":"Xiyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8092-6679","authenticated-orcid":false,"given":"Yuanfa","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1908-4481","authenticated-orcid":false,"given":"Jingjing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8796-2565","authenticated-orcid":false,"given":"Wentao","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5952-5598","authenticated-orcid":false,"given":"Songke","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7811-411X","authenticated-orcid":false,"given":"Wenbin","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1657-8408","authenticated-orcid":false,"given":"Xizi","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4363-5420","authenticated-orcid":false,"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"School of Aerospace, Harbin Institute of Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MSP.2019.2926573","article-title":"The rise of radar for autonomous vehicles: Signal processing solutions and future research directions","volume":"36","author":"Bilik","year":"2019","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schumann, O., Hahn, M., Scheiner, N., Weishaupt, F., Tilly, J.F., Dickmann, J., and W\u00f6hler, C. (2021, January 1\u20134). RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications. Proceedings of the 2021 IEEE 24th International Conference on Information Fusion (FUSION), Sun City, South Africa.","DOI":"10.23919\/FUSION49465.2021.9627037"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fent, F., Bauerschmidt, P., and Lienkamp, M. (2023, January 17\u201324). RadarGNN: Transformation Invariant Graph Neural Network for Radar-Based Perception. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00023"},{"key":"ref_4","unstructured":"Meyer, M., and Kuschk, G. (2019, January 2\u20134). Automotive Radar Dataset for Deep Learning Based 3D Object Detection. Proceedings of the European Radar Conference (EuRAD), Paris, France."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shi, W., and Rajkumar, R. (2020, January 13\u201319). Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","article-title":"The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains","volume":"30","author":"Shuman","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/JPROC.2018.2820126","article-title":"Graph Signal Processing: Overview, Challenges, and Applications","volume":"106","author":"Ortega","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_8","unstructured":"Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5\u201310). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Barcelona, Spain."},{"key":"ref_9","unstructured":"He, M., Wei, Z., Huang, Z., and Xu, H. (2021, January 6\u201314). BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Virtual."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hou, W., Li, W., and Li, P. (2023). Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion. Sensors, 23.","DOI":"10.3390\/s23115110"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, X., Sun, X., Li, J., Ji, Y., and Fu, W. (2026). Bounded Graph Conditioning for LiDAR 3D Object Detection Under Sensor Degradation. Sensors, 26.","DOI":"10.3390\/s26092667"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Liu, L., Zhao, H., L\u00f3pez-Ben\u00edtez, M., Yu, L., and Yue, Y. (2022). Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges. Sensors, 22.","DOI":"10.3390\/s22114208"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Abdu, F.J., Zhang, Y., Fu, M., Li, Y., and Deng, Z. (2021). Application of Deep Learning on Millimeter-Wave Radar Signals: A Review. Sensors, 21.","DOI":"10.3390\/s21061951"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, T., Yang, M., Jiang, K., Wong, H., and Yang, D. (2020). MMW Radar-Based Technologies in Autonomous Driving: A Review. Sensors, 20.","DOI":"10.3390\/s20247283"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Major, B., Fontijne, D., Ansari, A., Sukhavasi, R.T., Gowaiker, R., Hamilton, M., Lee, S., Grzechnik, S.K., and Subramanian, S. (2019, January 27\u201328). Vehicle Detection with Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00121"},{"key":"ref_16","unstructured":"Nabati, R., and Qi, H. (2020). Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles. arXiv."},{"key":"ref_17","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_18","first-page":"1","article-title":"Lightweight Local Transformer PointPillars: A 3-D Object Detection Framework for Autonomous Driving","volume":"early access","author":"Peng","year":"2026","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shi, W., Tong, P., and Bi, X. (2025). Moving-Least-Squares-Enhanced 3D Object Detection for 4D Millimeter-Wave Radar. Remote Sens., 17.","DOI":"10.3390\/rs17081465"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/TIV.2023.3322729","article-title":"SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar","volume":"9","author":"Liu","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zheng, L., Ma, Z., Zhu, X., Tan, B., Li, S., Long, K., Sun, W., Chen, S., Zhang, L., and Wan, M. (2022, January 8\u201312). TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","DOI":"10.1109\/ITSC55140.2022.9922539"},{"key":"ref_22","unstructured":"Paek, D.H., Kong, S.H., and Wijaya, K.T. (December, January 28). K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, New Orleans, LA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rebut, J., Ouaknine, A., Malik, W., and P\u00e9rez, P. (2022, January 18\u201324). Raw High-Definition Radar for Multi-Task Learning. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01651"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ouaknine, A., Newson, A., P\u00e9rez, P., Tupin, F., and Rebut, J. (2021, January 10\u201317). Multi-View Radar Semantic Segmentation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01538"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TRS.2025.3537604","article-title":"TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection","volume":"3","author":"Cheng","year":"2025","journal-title":"IEEE Trans. Radar Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, J., Gong, P., Wu, Y., and Zhou, M. (2024). Radar Point Cloud Target Classification Based on Graph Neural Network. Proceedings of the 2024 4th International Joint Conference on Robotics and Artificial Intelligence, ACM.","DOI":"10.1145\/3696474.3696732"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, B., Zhang, X., Wang, L., Hu, X., Li, Z., Pan, S., Li, J., and Deng, Y. (2021, January 19\u201322). RPFA-Net: A 4D Radar Pillar Feature Attention Network for 3D Object Detection. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564754"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, Z., Liu, Z., Xia, Z., Wang, X., Wang, Y., Qi, S., Dong, Y., Dong, N., Zhang, L., and Zhu, C. (2024, January 17\u201321). RCBEVDet: Radar-Camera Fusion in Bird\u2019s Eye View for 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01414"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ogunrinde, I., and Bernadin, S. (2023). Deep Camera\u2013Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Sensors, 23.","DOI":"10.20944\/preprints202305.2180.v1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17218","DOI":"10.1109\/TITS.2024.3421339","article-title":"Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors","volume":"25","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8379","DOI":"10.1109\/JSEN.2024.3357826","article-title":"YCANet: Target Detection for Complex Traffic Scenes Based on Camera-LiDAR Fusion","volume":"24","author":"Shen","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20864","DOI":"10.1109\/TITS.2025.3606554","article-title":"RM2Occ: Re-Projection Multi-Task Multi-Sensor Fusion for Autonomous Driving 3D Object Detection and Occupancy Perception","volume":"26","author":"Ren","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alazeb, A., Chughtai, B.R., Al Mudawi, N., AlQahtani, Y., Alonazi, M., Aljuaid, H., Jalal, A., and Liu, H. (2024). Remote Intelligent Perception System for Multi-Object Detection. Front. Neurorobotics, 18.","DOI":"10.3389\/fnbot.2024.1398703"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1145\/3326362","article-title":"Dynamic Graph CNN for Learning on Point Clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kong, L., Liu, Y., Li, X., Chen, R., Zhang, W., Ren, J., Pan, L., Chen, K., and Liu, Z. (2023, January 1\u20136). Robo3D: Towards Robust and Reliable 3D Perception against Corruptions. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01830"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., and Heide, F. (2020, January 13\u201319). Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"ref_37","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_38","unstructured":"Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2014, January 14\u201316). Spectral Networks and Locally Connected Networks on Graphs. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_39","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_40","unstructured":"Chien, E., Peng, J., Li, P., and Milenkovic, O. (2021, January 3\u20137). Adaptive Universal Generalized PageRank Graph Neural Network. Proceedings of the International Conference on Learning Representations (ICLR), Virtual Event."},{"key":"ref_41","unstructured":"Wang, X., and Zhang, M. (2022, January 17\u201323). How Powerful are Spectral Graph Neural Networks. Proceedings of the International Conference on Machine Learning (ICML), Baltimore, MD, USA."},{"key":"ref_42","unstructured":"Bo, D., Shi, C., Wang, L., and Liao, R. (2023, January 1\u20135). Specformer: Spectral Graph Neural Networks Meet Transformers. Proceedings of the International Conference on Learning Representations (ICLR), Kigali, Rwanda."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fu, X., Zhang, J., Meng, Z., and King, I. (2020, January 20\u201324). MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. Proceedings of the Web Conference (WWW) 2020, Taipei, Taiwan.","DOI":"10.1145\/3366423.3380297"},{"key":"ref_44","unstructured":"Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., and Battaglia, P. (2020, January 13\u201318). Learning to Simulate Complex Physics with Graph Networks. Proceedings of the 37th International Conference on Machine Learning, Virtual."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5991","DOI":"10.1109\/LRA.2025.3563127","article-title":"UE-Extractor: A Grid-to-Point Ground Extraction Framework for Unstructured Environments Using Adaptive Grid Projection","volume":"10","author":"Li","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4067","DOI":"10.1007\/s00371-024-03646-2","article-title":"MFINet: A Multi-Scale Feature Interaction Network for Point Cloud Registration","volume":"41","author":"Cao","year":"2025","journal-title":"Vis. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cheng, L., Guo, L., Zhang, T., Bang, T., Harris, A., Hajij, M., Sartipi, M., and Cao, S. (2025). CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR\u2013Camera Calibration with Iterative and Attention-Driven Post-Refinement. arXiv.","DOI":"10.2139\/ssrn.5164943"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liang, T., Liu, R., Yang, L., Lin, Y., Shi, C.J.R., and Xu, H. (2024). Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar. Sensors, 24.","DOI":"10.3390\/s24020648"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2022.12.021","article-title":"Perception and sensing for autonomous vehicles under adverse weather conditions: A survey","volume":"196","author":"Zhang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1109\/LRA.2022.3147324","article-title":"Multi-Class Road User Detection with 3+1D Radar in the View-of-Delft Dataset","volume":"7","author":"Palffy","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuScenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"121225","DOI":"10.1016\/j.measurement.2026.121225","article-title":"Sensor Selection Strategy and Multimodal Layout Optimization for Autonomous Vehicles: A Review","volume":"274","author":"Yan","year":"2026","journal-title":"Measurement"},{"key":"ref_53","first-page":"1","article-title":"Autonomous Vehicles","volume":"1","year":"2020","journal-title":"Encyclopedia of Information Science and Technology"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4714\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T04:20:49Z","timestamp":1785298849000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4714"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":53,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26154714"],"URL":"https:\/\/doi.org\/10.3390\/s26154714","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,24]]}}}