{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:11:25Z","timestamp":1771564285087,"version":"3.50.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>With the increasing prevalence of electric vehicles, motion sickness has emerged as a critical factor impairing passenger comfort. Current studies relying on simulated driving face limitations in replicating real-road conditions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted real-vehicle experiments across six roadway scenarios: one-way left turn (R1), linear acceleration\/deceleration (R2), sudden arrest-activation (R3), uphill S-curve (R4), downhill S-curve (R5), and one-way right turn (R6). A synchronized system (BioRadio + vehicle gyroscopes) captured subjective ratings from participants (<jats:italic>n<\/jats:italic>\u202f=\u202f10) and objective data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Significant changes occurred in mean values of <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi mathvariant=\"italic\">GS<\/mml:mi><mml:msub><mml:mi>R<\/mml:mi><mml:mtext mathvariant=\"italic\">mean<\/mml:mtext><\/mml:msub><\/mml:math><\/jats:inline-formula>,<jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mi>H<\/mml:mi><mml:msub><mml:mi>R<\/mml:mi><mml:mtext mathvariant=\"italic\">mean<\/mml:mtext><\/mml:msub><\/mml:math><\/jats:inline-formula>, RMSSD, and <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mi mathvariant=\"italic\">RES<\/mml:mi><mml:msub><mml:mi>P<\/mml:mi><mml:mtext mathvariant=\"italic\">mean<\/mml:mtext><\/mml:msub><\/mml:math><\/jats:inline-formula> during motion sickness (<jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.05), while standard deviations (<jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mi mathvariant=\"italic\">GS<\/mml:mi><mml:msub><mml:mi>R<\/mml:mi><mml:mi mathvariant=\"italic\">SD<\/mml:mi><\/mml:msub><\/mml:math><\/jats:inline-formula>, <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mi mathvariant=\"italic\">RES<\/mml:mi><mml:msub><mml:mi>P<\/mml:mi><mml:mi mathvariant=\"italic\">SD<\/mml:mi><\/mml:msub><\/mml:math><\/jats:inline-formula>) showed no significance. Motion sickness severity ranked as: R4 (8.4)\u202f&amp;gt;\u202fR5 (7.7)\u202f&amp;gt;\u202fR3 (6.3)\u202f&amp;gt;\u202fR2 (4.4)\u202f&amp;gt;\u202fR1 (2.0)\u202f&amp;gt;\u202fR6 (1.4), confirming S-curves as the primary trigger.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The logistic regression model achieved 81.25% accuracy in predicting motion sickness states. This study provides empirical evidence for optimizing vehicle motion control and road design to enhance passenger comfort.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpsyg.2025.1615498","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T12:55:38Z","timestamp":1754052938000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Investigating the impact of different road scenarios on the induction intensity of motion sickness in electric vehicle passengers"],"prefix":"10.3389","volume":"16","author":[{"given":"Bangbei","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingjie","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuyang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingzhang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1001080","DOI":"10.3389\/frvir.2022.1001080","article-title":"Using quantitative data on postural activity to develop methods to predict and prevent cybersickness","volume":"3","author":"Bailey","year":"2022","journal-title":"Front. 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