{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:18:24Z","timestamp":1779135504920,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T00:00:00Z","timestamp":1606867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["cstc2018jcyjA3148"],"award-info":[{"award-number":["cstc2018jcyjA3148"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An accurate vehicle driving state observer is a necessary condition for a safe automotive electronic control system. Vehicle driving state observer is challenged by unknown measurement noise and transient disturbances caused by complex working conditions and sensor failure. For the classical adaptive unscented Kalman filter (AUKF) algorithm, transient disturbances will cause the failure of state estimation and affect the subsequent process. This paper proposes an AUKF based on a modified Sage\u2013Husa filter and divergence calculation technique for multi-dimensional vehicle driving state observation. Based on the seven-degrees-of-freedom vehicle model and the Dugoff tire model, the proposed algorithm corrects the measurement noise by using modified Sage\u2013Husa maximum posteriori. To reduce the influence of transient disturbance on the subsequent process, covariance matrix is updated after divergence is detected. The effectiveness of the algorithm is tested on the double lane change and Sine Wave road conditions. The robustness of the algorithm is tested under severe transient disturbance. The results demonstrate that the modified Sage\u2013Husa UKF algorithm can accurately detect transient disturbance and effectively reduce the resulted accumulated error. Compared to classical AUKF, our algorithm significantly improves the accuracy and robustness of vehicle driving state estimation. The research in this paper provides a reference for multi-dimensional data processing under changeable vehicle driving states.<\/jats:p>","DOI":"10.3390\/s20236889","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T07:49:54Z","timestamp":1606895394000},"page":"6889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage\u2013Husa UKF Algorithm"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1160-219X","authenticated-orcid":false,"given":"Zeyuan","family":"Luo","sequence":"first","affiliation":[{"name":"Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3366-6522","authenticated-orcid":false,"given":"Zanhao","family":"Fu","sequence":"additional","affiliation":[{"name":"Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiwei","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment &amp; System Security and New Technology, Chongqing University, Chongqing 400030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,2]]},"reference":[{"key":"ref_1","unstructured":"(2020, October 26). 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