Fatigue driving state detection for tanker truck drivers based on multi-feature fusion

Ning Zhang; Ziyi Zhang; Chuanyi Ma; Ziliang Yang; Shengtao Zhang; Jianqing Wu

doi:10.1117/12.3030354

16 May 2024 Fatigue driving state detection for tanker truck drivers based on multi-feature fusion

Ning Zhang, Ziyi Zhang, Chuanyi Ma, Ziliang Yang, Shengtao Zhang, Jianqing Wu

Proceedings Volume 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024); 131600R (2024) https://doi.org/10.1117/12.3030354
Event: 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 2024, Beijin, China

Abstract

As the significance of truck transportation in the modern economy continues to grow, the issue of fatigue driving in tanker trucks has garnered significant attention. Therefore, this study proposes a multimodal fatigue driving detection method. It involves conducting driving experiments with tanker trucks, collecting driving operation data, electrocardiogram data, and eye-tracking data. After data preprocessing, a multimodal driving dataset is generated. Data mining techniques are used to extract 42 driving feature values, and then, through correlation analysis, 27 feature values are selected for fatigue state detection. Subsequently, the K-means method is employed to classify driving data into four fatigue levels, and a random forest algorithm is used for fatigue state recognition. Experimental results demonstrate that the proposed fatigue detection algorithm achieves a precision rate of 92.6%, effectively identifying different fatigue driving states. This approach provides insights and theoretical support for subsequent driver risk assessment and targeted traffic management.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ning Zhang, Ziyi Zhang, Chuanyi Ma, Ziliang Yang, Shengtao Zhang, and Jianqing Wu "Fatigue driving state detection for tanker truck drivers based on multi-feature fusion", Proc. SPIE 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 131600R (16 May 2024); https://doi.org/10.1117/12.3030354

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