Advancements and Perspectives in Fatigue Driving Detection: A Comprehensive Review

Xiao Yan; Ashardi bin Abas

doi:10.62762/TIUS.2024.767724

Volume 1, Issue 1, ICCK Transactions on Intelligent Unmanned Systems

Volume 1, Issue 1, 2024

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School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

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ICCK Transactions on Intelligent Unmanned Systems, Volume 1, Issue 1, 2024: 4-15

Free to Read | Review Article | 07 July 2024

Advancements and Perspectives in Fatigue Driving Detection: A Comprehensive Review

Xiao Yan 1 *

Ashardi bin Abas 1

1 University Pendidikan Sultan Idrts, 35900, Malaysia

* Corresponding Author: Xiao Yan, [email protected]

DOI: 10.62762/TIUS.2024.767724

ARK: ark:/57805/tius.2024.767724

Received: 19 April 2024, Accepted: 25 June 2024, Published: 07 July 2024

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Abstract

Driver fatigue is a significant contributor to road accidents worldwide. Timely detection and alert systems for driver fatigue can substantially enhance driving safety and reduce traffic-related casualties. This article presents a comprehensive review of the recent advancements in driver fatigue detection technologies. It categorizes and evaluates detection methods based on physiological signals, behavioral characteristics, vehicle dynamics, and information fusion techniques. Additionally, it scrutinizes the prevalent datasets and methodologies employed in fatigue detection, offering valuable insights for future research directions. Our analysis emphasizes the importance of integrating multimodal data to improve detection accuracy and reliability, underlining the potential of information fusion approaches in developing robust fatigue detection systems. This synthesis aims to serve as a foundational reference for researchers venturing into the domain of driver fatigue detection, paving the way for innovative solutions to combat fatigue-induced road accidents.

Graphical Abstract

Keywords

Fatigue driving

Detection method

Information fusion

Dataset

Funding

This work was supported without any funding.

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Yan, X., & Abas, A. (2024). Advancements and Perspectives in Fatigue Driving Detection: A Comprehensive Review. ICCK Transactions on Intelligent Unmanned Systems, 1(1), 4–12 https://doi.org/10.62762/TIUS.2024.767724

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TY  - JOUR
AU  - Yan, Xiao
AU  - Abas, Ashardi bin
PY  - 2024
DA  - 2024/07/07
TI  - Advancements and Perspectives in Fatigue Driving Detection: A Comprehensive Review
JO  - ICCK Transactions on Intelligent Unmanned Systems
T2  - ICCK Transactions on Intelligent Unmanned Systems
JF  - ICCK Transactions on Intelligent Unmanned Systems
VL  - 1
IS  - 1
SP  - 4
EP  - 15
DO  - 10.62762/TIUS.2024.767724
UR  - https://www.icck.org/article/abs/TIUS.2024.767724
KW  - Fatigue driving
KW  - Detection method
KW  - Information fusion
KW  - Dataset
AB  - Driver fatigue is a significant contributor to road accidents worldwide. Timely detection and alert systems for driver fatigue can substantially enhance driving safety and reduce traffic-related casualties. This article presents a comprehensive review of the recent advancements in driver fatigue detection technologies. It categorizes and evaluates detection methods based on physiological signals, behavioral characteristics, vehicle dynamics, and information fusion techniques. Additionally, it scrutinizes the prevalent datasets and methodologies employed in fatigue detection, offering valuable insights for future research directions. Our analysis emphasizes the importance of integrating multimodal data to improve detection accuracy and reliability, underlining the potential of information fusion approaches in developing robust fatigue detection systems. This synthesis aims to serve as a foundational reference for researchers venturing into the domain of driver fatigue detection, paving the way for innovative solutions to combat fatigue-induced road accidents.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Yan2024Advancemen,
  author = {Xiao Yan and Ashardi bin Abas},
  title = {Advancements and Perspectives in Fatigue Driving Detection: A Comprehensive Review},
  journal = {ICCK Transactions on Intelligent Unmanned Systems},
  year = {2024},
  volume = {1},
  number = {1},
  pages = {4-15},
  doi = {10.62762/TIUS.2024.767724},
  url = {https://www.icck.org/article/abs/TIUS.2024.767724},
  abstract = {Driver fatigue is a significant contributor to road accidents worldwide. Timely detection and alert systems for driver fatigue can substantially enhance driving safety and reduce traffic-related casualties. This article presents a comprehensive review of the recent advancements in driver fatigue detection technologies. It categorizes and evaluates detection methods based on physiological signals, behavioral characteristics, vehicle dynamics, and information fusion techniques. Additionally, it scrutinizes the prevalent datasets and methodologies employed in fatigue detection, offering valuable insights for future research directions. Our analysis emphasizes the importance of integrating multimodal data to improve detection accuracy and reliability, underlining the potential of information fusion approaches in developing robust fatigue detection systems. This synthesis aims to serve as a foundational reference for researchers venturing into the domain of driver fatigue detection, paving the way for innovative solutions to combat fatigue-induced road accidents.},
  keywords = {Fatigue driving, Detection method, Information fusion, Dataset},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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