Lane-change intention prediction using eye-tracking technology: A systematic review.
The aim of this study is to identify the best practices and future research directions for driver lane-change intention (DLCI) prediction using eye-tracking technologies based on a systematic literature review. We searched five academic literature databases and then conducted an in-depth review, str...
| Publicado en: | Applied Ergonomics Vol. 103 |
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| Autores principales: | , , , , , , |
| Formato: | review Journal Article |
| Publicado: |
Elsevier B.V.
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157120454&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157120454 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00036870 ALO jtl: Applied Ergonomics issn: 00036870 maglogo: N pubinfo: dt: Sep2022 vid: 103 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 157120454 157120454 NLM35500523 157120454 10.1016/j.apergo.2022.103775 NLM35500523 157120454 ppct: 1 formats: tig: atl: Lane-change intention prediction using eye-tracking technology: A systematic review. aug: au: Pan, Yunxian Zhang, Qinyu Zhang, Yifan Ge, Xianliang Gao, Xiaoqing Yang, Shiyan Xu, Jie affil: Center for Psychological Sciences, Zhejiang University, Hangzhou, Zhejiang Province, PR China sug: ab: The aim of this study is to identify the best practices and future research directions for driver lane-change intention (DLCI) prediction using eye-tracking technologies based on a systematic literature review. We searched five academic literature databases and then conducted an in-depth review, structured coding, and analysis of 40 relevant articles. The literature on DLCI prediction is summarized in terms of input features, feature extraction and prediction time windows, labeling methods, and machine learning algorithms. The results show that eye tracking data features along with other data sources can be useful inputs for the prediction of DLCI. Major challenges in this line of research include determining the optimal time window for feature extraction and developing and evaluating the appropriate machine learning algorithm. Suggestions for future research and practice for DLCI prediction in intelligent vehicles are discussed. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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