{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T01:58:21Z","timestamp":1792288701306,"version":"4.4.0"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Generation of Information Technology Innovation Project of the China University Innovation Fund of the Ministry of Education","award":["2022IT191"],"award-info":[{"award-number":["2022IT191"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers\u2019 life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.<\/jats:p>","DOI":"10.3390\/s24237529","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T05:06:53Z","timestamp":1732597613000},"page":"7529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Identification Method for Road Hypnosis Based on the Fusion of Human Life Parameters"],"prefix":"10.3390","volume":"24","author":[{"given":"Bin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Ohio State University, Columbus, OH 43220, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2418-7394","authenticated-orcid":false,"given":"Xiaoyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0065-1001","authenticated-orcid":false,"given":"Longfei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Jiao","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.aap.2017.09.022","article-title":"Multilevel analysis of the role of human factors in regional disparities in crash outcomes","volume":"109","author":"Adanu","year":"2017","journal-title":"Accident. 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