{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:24:12Z","timestamp":1774628652052,"version":"3.50.1"},"reference-count":46,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p> In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (\u221224% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (\u221229% Root Mean Squared Error with lidar and \u221238% Root Mean Squared Error with radar). <\/jats:p>","DOI":"10.1177\/0959651820975523","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T18:25:12Z","timestamp":1607624712000},"page":"1125-1138","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":36,"title":["Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles"],"prefix":"10.1177","volume":"235","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9191-1824","authenticated-orcid":false,"given":"Wael","family":"Farag","sequence":"first","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Al-Eqaila, Kuwait"},{"name":"Department of Electrical Engineering, Cairo University, Cairo, Egypt"}]}],"member":"179","published-online":{"date-parts":[[2020,12,8]]},"reference":[{"issue":"3","key":"bibr1-0959651820975523","first-page":"215","volume":"13","author":"Farag W.","year":"2019","journal-title":"Intel Decis Tech"},{"key":"bibr2-0959651820975523","volume-title":"International conference on innovation and intelligence for informatics, computing, and technologies (3ICT)","author":"Farag W"},{"key":"bibr3-0959651820975523","doi-asserted-by":"publisher","DOI":"10.12785\/ijcds\/090302"},{"key":"bibr4-0959651820975523","volume-title":"International conference on innovation and intelligence for informatics, computing, and technologies (3ICT)","author":"Farag W"},{"key":"bibr5-0959651820975523","doi-asserted-by":"publisher","DOI":"10.3233\/KES-180385"},{"key":"bibr6-0959651820975523","volume-title":"International conference on innovation and intelligence for informatics, computing, and technologies (3ICT)","author":"Farag W"},{"key":"bibr7-0959651820975523","doi-asserted-by":"publisher","DOI":"10.1007\/s13177-019-00204-2"},{"key":"bibr8-0959651820975523","doi-asserted-by":"publisher","DOI":"10.1007\/s13177-020-00226-1."},{"key":"bibr9-0959651820975523","volume-title":"2nd smart cities symposium (SCS\u201919)","author":"Farag W"},{"key":"bibr10-0959651820975523","doi-asserted-by":"publisher","DOI":"10.1504\/IJAMECHS.2017.099318"},{"key":"bibr11-0959651820975523","doi-asserted-by":"publisher","DOI":"10.2174\/2213275911666181106160002"},{"key":"bibr12-0959651820975523","volume-title":"2nd smart cities symposium (SCS\u201919)","author":"Farag W"},{"key":"bibr13-0959651820975523","doi-asserted-by":"crossref","unstructured":"Yurtsever E, Lambert J, Carballo A, et al. 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