{
  "abstract": "SAE Level 3 automated driving systems assume full control of the dynamic driving task within their operational design domain. Ensuring a safe and timely transition from automated to manual driving therefore necessitates continuous monitoring and assessment of driver state. This review presents a synthesis of 85 studies published mainly between 2021 and 2025. A PRISMA 2020-guided search was conducted in ScienceDirect, Web of Science, SpringerLink and IEEE Xplore, with the final search completed on 15th September 2025. Only English-language publications that examined driver-state detection, recognition or assessment in driving scenarios relevant to conditional automation were considered. Studies published before 2021 were excluded, unless they provided foundational contributions. This review was not registered. This review aims to (1) identify driver-state constructs that have been investigated in conditional automated driving, examining how they are defined and operationalized; (2) characterize sensing modalities used for driver-state monitoring; (3) evaluate computational approaches including multimodal fusion used in previous studies; (4) examine performance metrics, validation strategies and real-time feasibility; (5) identify methodological limitations, reporting inconsistencies and priorities for future development and deployment. This study suggests the standardisation of Level 3 datasets. It also calls for harmonised operational definitions and more representative sampling. It finally advises the performance of more longitudinal on-road validation. This work was supported by the National Natural Science Foundation of China.",
  "abstract_shown": true,
  "affiliations": [
    "College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan, China",
    "Key Laboratory of Automotive Power Train and Electronic Control, Hubei University of Automotive Technology, Shiyan, China",
    "Department of Breast and thyroid Surgery, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
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      "name": "Haoran Wu"
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      "name": "Timothy Tettey Nartey"
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      "name": "Guangjun Wan"
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    {
      "name": "Yugang Wang"
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    {
      "name": "Linli Xu"
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    {
      "name": "Xun Zhou"
    }
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  "licence_label": "CC BY 4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "/Medicine and health sciences/Clinical medicine/Clinical neurophysiology/Electroencephalography"
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      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Brain electrophysiology/Electroencephalography"
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    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Cardiac electrophysiology/Electrocardiography"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358700&type=printable",
  "pmcid": null,
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  "published": "2026-09-17",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/4gsgp95wnkgzx.ris",
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    {
      "first_seen": "2026-10-30T12:17:49Z",
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  "subjects": [
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      "subfield": "neuroscience"
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  "title": "Driver state detection and recognition in conditional automated driving: A review",
  "title_text": "Driver state detection and recognition in conditional automated driving: A review",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/4gsgp95wnkgzx",
  "venue": "PLOS One",
  "versions": []
}