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Peer-reviewedCC BY 4.0PLOS One

Driver state detection and recognition in conditional automated driving: A review

  • Haoran Wu1
  • Timothy Tettey Nartey2
  • Guangjun Wan3
  • Yugang Wang
  • Linli Xu
  • Xun Zhou
  1. 1College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan, China
  2. 2Key Laboratory of Automotive Power Train and Electronic Control, Hubei University of Automotive Technology, Shiyan, China
  3. 3Department of Breast and thyroid Surgery, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
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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.

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Cite

BibTeXRIS
BibTeX
@article{wu2026driver,
  title = {{Driver state detection and recognition in conditional automated driving: A review}},
  author = {Haoran Wu and Timothy Tettey Nartey and Guangjun Wan and Yugang Wang and Linli Xu and Xun Zhou},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0358700},
  url = {https://doi.org/10.1371/journal.pone.0358700},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Driver state detection and recognition in conditional automated driving: A review
AU  - Haoran Wu
AU  - Timothy Tettey Nartey
AU  - Guangjun Wan
AU  - Yugang Wang
AU  - Linli Xu
AU  - Xun Zhou
PY  - 2026
DA  - 2026/09/17
JO  - PLOS One
DO  - 10.1371/journal.pone.0358700
UR  - https://doi.org/10.1371/journal.pone.0358700
AB  - 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.
SN  - 1932-6203
ER  -