{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:33:32Z","timestamp":1783791212901,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund","award":["KDP-10-3\/PALY-2022"],"award-info":[{"award-number":["KDP-10-3\/PALY-2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle\u2019s yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.<\/jats:p>","DOI":"10.3390\/s23125670","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T02:29:19Z","timestamp":1687141759000},"page":"5670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9676-0797","authenticated-orcid":false,"given":"J\u00e1nos","family":"Kontos","sequence":"first","affiliation":[{"name":"Continental Automotive Hungary Ltd., 8200 Veszpr\u00e9m, Hungary"},{"name":"Department of Computer Science and Systems Technology, University of Pannonia, 8200 Veszpr\u00e9m, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bal\u00e1zs","family":"Kr\u00e1nicz","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Pannonia, 8200 Veszpr\u00e9m, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5524-1675","authenticated-orcid":false,"given":"\u00c1gnes","family":"Vathy-Fogarassy","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Systems Technology, University of Pannonia, 8200 Veszpr\u00e9m, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","unstructured":"(2018). 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