{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:39:24Z","timestamp":1772602764723,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Planning Project of Xi\u2019an city","award":["21XJZZ0023"],"award-info":[{"award-number":["21XJZZ0023"]}]},{"name":"Doctoral Research Start-up Fund Project of Xi\u2019an Polytechnic University","award":["21XJZZ0023"],"award-info":[{"award-number":["21XJZZ0023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the development of intelligent transportation, vehicle motion state recognition has become a crucial method for enhancing the reliability of vehicle navigation and ensuring driving safety. Currently, machine learning is the main approach for recognizing vehicle motion states. The symmetry characteristics of sensor data have also been studied to better recognize motion states. However, the existing approaches face challenges during motion state changes due to indeterminate state boundaries, resulting in reduced recognition accuracy. To address this problem, this paper proposes a vehicle motion state recognition method based on the Hidden Markov Model (HMM) and Support Vector Machine (SVM). Firstly, Kalman filtering is applied to denoise the data of inertial sensors. Then, HMM is employed to capture the subtle state transition, enabling the recognition of complex dynamic state changes. Finally, SVM is utilized to classify motion states. The sensor data were collected in various vehicle motion states, including stationary, straight-line driving, lane changing, turning, and then the proposed method is compared with SVM, KNN (K-Nearest Neighbor), DT (Decision Tree), RF (Random Forest), and NB (Naive Bayes). The results of the experiment show that the proposed method improves the recognition accuracy of motion state transitions in the case of boundary ambiguity and is superior to the existing methods.<\/jats:p>","DOI":"10.3390\/sym17071011","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T05:29:58Z","timestamp":1751002198000},"page":"1011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vehicle Motion State Recognition Method Based on Hidden Markov Model and Support Vector Machine"],"prefix":"10.3390","volume":"17","author":[{"given":"Xiaojun","family":"Zou","sequence":"first","affiliation":[{"name":"School of Electronic Information, Xi\u2019an Polytechnic University, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weibo","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Xi\u2019an Polytechnic University, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihong","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Xi\u2019an Polytechnic University, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"En","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Xi\u2019an Polytechnic University, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9008-0061","authenticated-orcid":false,"given":"Chengkai","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.renene.2023.01.004","article-title":"Adaptive Rule Control Strategy for Composite Energy Storage Fuel Cell Vehicle Based on Vehicle Operating State Recognition","volume":"204","author":"Hu","year":"2023","journal-title":"Renew. 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