{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T22:48:08Z","timestamp":1774219688931,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,21]],"date-time":"2017-10-21T00:00:00Z","timestamp":1508544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>New models and methods have been designed to predict the influence of the user\u2019s environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers\u2019 activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users\u2019 activity. The driving style was predicted from the user\u2019s environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user\u2019s environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts.<\/jats:p>","DOI":"10.3390\/s17102404","type":"journal-article","created":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T04:32:19Z","timestamp":1508733139000},"page":"2404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Estimation of the Driving Style Based on the Users\u2019 Activity and Environment Influence"],"prefix":"10.3390","volume":"17","author":[{"given":"Mikhail","family":"Sysoev","sequence":"first","affiliation":[{"name":"Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka cesta 25, Ljubljana 1000, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrej","family":"Kos","sequence":"additional","affiliation":[{"name":"Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka cesta 25, Ljubljana 1000, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5161-7751","authenticated-orcid":false,"given":"Jo\u017ee","family":"Guna","sequence":"additional","affiliation":[{"name":"Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka cesta 25, Ljubljana 1000, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matev\u017e","family":"Poga\u010dnik","sequence":"additional","affiliation":[{"name":"Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka cesta 25, Ljubljana 1000, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Porkodi, R., and Bhuvaneswari, V. 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