{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T02:00:09Z","timestamp":1778896809817,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,8,31]],"date-time":"2016-08-31T00:00:00Z","timestamp":1472601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Government through the CICYT project","award":["TRA2013-48030-C2-1-R."],"award-info":[{"award-number":["TRA2013-48030-C2-1-R."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a \u201cpseudo-roll angle\u201d through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors\u2019 estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.<\/jats:p>","DOI":"10.3390\/s16091400","type":"journal-article","created":{"date-parts":[[2016,8,31]],"date-time":"2016-08-31T13:11:45Z","timestamp":1472649105000},"page":"1400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5478-9287","authenticated-orcid":false,"given":"Leandro","family":"Vargas-Mel\u00e9ndez","sequence":"first","affiliation":[{"name":"Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, Madrid 28911, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8061-068X","authenticated-orcid":false,"given":"Beatriz","family":"Boada","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, Madrid 28911, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5377-0023","authenticated-orcid":false,"given":"Mar\u00eda","family":"Boada","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, Madrid 28911, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Gauch\u00eda","sequence":"additional","affiliation":[{"name":"Mechanical Engineering-Engineering Mechanics Department, Michigan Tech University, 1400 Townsend Drive, Houghton 49931, Michigan, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vicente","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, Madrid 28911, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1243\/09544070JAUTO124","article-title":"Integrated control of front-wheel steering and front braking forces on the basis of fuzzy logic","volume":"220","author":"Boada","year":"2006","journal-title":"J. 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