{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T05:00:49Z","timestamp":1780203649578,"version":"3.54.0"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"crossref","award":["2025D01C300"],"award-info":[{"award-number":["2025D01C300"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Fatigued driving is a key contributing factor to major traffic accidents. Existing detection technologies suffer from issues such as delayed identification, high error rates, and a lack of quantified causal relationships between physiological and behavioral indicators. This study aims to clarify the intrinsic relationship between electrophysiological and driving behavior data during the progression of driving fatigue. Methods: Four categories of driving behavior data and electrocardiographic (ECG) heart rate variability (HRV) indicators were selected as the study subjects. Based on a four-stage standardized simulated driving experiment ranging from wakefulness to severe fatigue, the correlations between indicators were quantified using Pearson correlation analysis, and a four-layer physiological\u2013behavioral fusion fatigue assessment model was constructed. Results: Autonomic dysregulation is the intrinsic cause of abnormal driving behavior. The two exhibit a highly synchronized, stepwise progressive evolution pattern, with |r| \u2265 0.75 among core indicators. The accuracy of the constructed model exceeded 90% for all fatigue stages, reaching 97.8% for severe fatigue detection, with a response time of \u22640.5 s. Conclusions: This model effectively addresses the limitations of single-monitoring technologies and provides theoretical support and technical guidance for multimodal identification and graded early warning of driving fatigue.<\/jats:p>","DOI":"10.3390\/s26113441","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T17:36:18Z","timestamp":1780076178000},"page":"3441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Study on the Correlation Between Driving Behavior and ECG Data in Driving Fatigue"],"prefix":"10.3390","volume":"26","author":[{"given":"Jiayou","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haocheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"He","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"ref_1","first-page":"159","article-title":"Analysis of Driving Fatigue Detection Based on Multi-Scale Wavelet Logarithmic Energy Entropy of Frontal EEG","volume":"33","author":"Min","year":"2020","journal-title":"China J. 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