{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:17:16Z","timestamp":1791915436241,"version":"4.3.4"},"reference-count":33,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["PNRR M4C2"],"award-info":[{"award-number":["PNRR M4C2"]}]},{"name":"European Union","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]},{"name":"European Union","award":["CUP E13C22000980001"],"award-info":[{"award-number":["CUP E13C22000980001"]}]},{"name":"Investimento 1.4","award":["PNRR M4C2"],"award-info":[{"award-number":["PNRR M4C2"]}]},{"name":"Investimento 1.4","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]},{"name":"Investimento 1.4","award":["CUP E13C22000980001"],"award-info":[{"award-number":["CUP E13C22000980001"]}]},{"name":"Sustainable Mobility Center (Centro Nazionale per la Mobilit\u00e0 Sostenibile)","award":["PNRR M4C2"],"award-info":[{"award-number":["PNRR M4C2"]}]},{"name":"Sustainable Mobility Center (Centro Nazionale per la Mobilit\u00e0 Sostenibile)","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]},{"name":"Sustainable Mobility Center (Centro Nazionale per la Mobilit\u00e0 Sostenibile)","award":["CUP E13C22000980001"],"award-info":[{"award-number":["CUP E13C22000980001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fusion of multiple sensors\u2019 data in real-time is a crucial process for autonomous and assisted driving, where high-level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars, and LiDARs. The target application is an Advanced Driving Assistance System (ADAS) that benefits from the integration of the sensors\u2019 attributes to plan the vehicle\u2019s speed according to real-time road occupation and distance from obstacles. Based on geometrical projection, a low-level sensor fusion approach is proposed to map 3D point clouds into 2D camera images. The fusion information is used to estimate the distance of objects detected and labeled by a Yolov7 detector. The open-source pipeline implemented in ROS consists of a sensors\u2019 calibration method, a Yolov7 detector, 3D point cloud downsampling and clustering, and finally a 3D-to-2D transformation between the reference frames. The goal of the pipeline is to perform data association and estimate the distance of the identified road objects. The accuracy and performance are evaluated in real-world urban scenarios with commercial hardware. The pipeline running on an embedded Nvidia Jetson AGX achieves good accuracy on object identification and distance estimation, running at 5 Hz. The proposed framework introduces a flexible and resource-efficient method for data association from common automotive sensors and proves to be a promising solution for enabling effective environment perception ability for assisted driving.<\/jats:p>","DOI":"10.3390\/s24247895","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T11:17:20Z","timestamp":1733829440000},"page":"7895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Sensor Fusion Method for Object Detection and Distance Estimation in Assisted Driving Applications"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4409-8380","authenticated-orcid":false,"given":"Stefano","family":"Favelli","sequence":"first","affiliation":[{"name":"Center for Automotive Research and Sustainable Mobility (CARS@PoliTO), Politecnico di Torino, 10129 Torino, Italy"},{"name":"Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, 10129 Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7502-5287","authenticated-orcid":false,"given":"Meng","family":"Xie","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, 10129 Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6621-5037","authenticated-orcid":false,"given":"Andrea","family":"Tonoli","sequence":"additional","affiliation":[{"name":"Center for Automotive Research and Sustainable Mobility (CARS@PoliTO), Politecnico di Torino, 10129 Torino, Italy"},{"name":"Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, 10129 Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rosique, F., Navarro, P.J., Fern\u00e1ndez, C., and Padilla, A. 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