{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:07:42Z","timestamp":1787029662919,"version":"build-2736575974"},"reference-count":16,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Agency for Infrastructure Technology Advancement (KAIA)","award":["RS-2021-KA160501"],"award-info":[{"award-number":["RS-2021-KA160501"]}]},{"name":"Ministry of Land, Infrastructure and Transport","award":["RS-2021-KA160501"],"award-info":[{"award-number":["RS-2021-KA160501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The widespread deployment of Cooperative Intelligent Transport Systems (C-ITS) has elevated the need for robust, real-time sensor fusion strategies capable of handling noisy, asynchronous data from multiple infrastructure sensors. In this paper, we propose a two-stage data fusion framework that integrates a grid-based indexing method for efficient duplicate-object detection with a Light Gradient Boosting Machine (LGBM) augmented by an Extended Kalman Filter (EKF). In the first stage, the hybrid EKF\u2013LGBM model mitigates noise, refines object trajectories, and synchronizes sensor streams under varying noise conditions. In the second stage, the grid-based indexing technique rapidly associates objects detected by multiple sensors, merging their measurements into unified state estimates. Extensive experiments\u2014using both synthetic data, where noise scales ranged from 0.5 to 3, and a real-road dataset\u2014confirm that our approach balances near-real-time performance with significantly improved trajectory accuracy. For instance, at a noise scale of 1, the hybrid method outperforms the Unscented Kalman Filter (UKF) while running up to 1.81 times faster, and real-world tests show a 1.54 times RMSE improvement over baseline measurements. By efficiently filtering out noise and minimizing the computational overhead of pairwise comparisons, the proposed system demonstrates practical feasibility with respect to C-ITS applications.<\/jats:p>","DOI":"10.3390\/s25071975","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T06:21:38Z","timestamp":1742797298000},"page":"1975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Efficient Multi-Sensor Fusion for Cooperative Autonomous Vehicles Leveraging C-ITS Infrastructure and Machine Learning"],"prefix":"10.3390","volume":"25","author":[{"given":"Jiwon","family":"Kwak","sequence":"first","affiliation":[{"name":"School of Cbersecurity, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayoung","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Korea National University of Transportation, Daehakro 50, Chungju 27469, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seokil","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Korea National University of Transportation, Daehakro 50, Chungju 27469, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"129","DOI":"10.14801\/jkiit.2023.21.12.129","article-title":"Design and Performance Analysis of 5G NR V2X-Based Sensor Data Sharing System","volume":"21","author":"Ji","year":"2023","journal-title":"J. KIIT"},{"key":"ref_2","first-page":"100980","article-title":"Vehicle-to-Everything (V2X) in the Autonomous Vehicles Domain\u2013A Technical Review of Communication, Sensor, and AI Technologies for Road User Safety","volume":"23","author":"Yusuf","year":"2024","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"\u010cvek, K., Mostarac, M., and Mili\u010devi\u0107, K. (2022, January 19\u201321). Impact of Different Noise Distributions in the Application of Kalman Filter in Sensor Fusion. Proceedings of the 2022 International Conference on Smart Systems and Technologies (SST), Osijek, Croatia.","DOI":"10.1109\/SST55530.2022.9954868"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2330","DOI":"10.1049\/cmu2.12273","article-title":"An Efficient Multi-Sensor Fusion and Tracking Protocol in a Vehicle-Road Collaborative System","volume":"15","author":"Xu","year":"2021","journal-title":"IET Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/TIE.2022.3150077","article-title":"A Novel Plug-and-Play Factor Graph Method for Asynchronous Absolute\/Relative Measurements Fusion in Multisensor Positioning","volume":"70","author":"Bai","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","first-page":"319","article-title":"Multisensor Image Fusion: Dataset, Methods, and Performance Evaluation","volume":"23","author":"Laidouni","year":"2024","journal-title":"J. Opt. Therm. 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Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, Wiley-Interscience.","DOI":"10.1002\/0470045345"},{"key":"ref_11","first-page":"3146","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","volume":"Volume 30","author":"Ke","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102562","DOI":"10.1016\/j.apor.2021.102562","article-title":"A New Multi-Sensor Hierarchical Data Fusion Algorithm Based on Unscented Kalman Filter for the Attitude Observation of the Wave Glider","volume":"109","author":"Liu","year":"2021","journal-title":"Appl. Ocean Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tian, K., Radovnikovich, M., and Cheok, K. (2022, January 25\u201328). Comparing EKF, UKF, and PF Performance for Autonomous Vehicle Multi-Sensor Fusion and Tracking in Highway Scenario. 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