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<p>Fatigue driving is one of the primary causes of traffic accidents, posing a serious threat not only to the driver’s life and property but also to the safety of surrounding vehicles and pedestrians. Therefore, accurate and efficient detection of driver fatigue is essential for preventing traffic incidents. This study proposes a novel driver fatigue detection framework that integrates AlphaPose* with a Long Short-Term Memory (LSTM) network. To enhance the performance of AlphaPose, the original human detector is replaced with an optimized YOLOv11n model, which incorporates a Hybrid Pooling Fusion Block (HPFB) to improve feature representation and meet the requirements of keypoint estimation. Furthermore, a multi-view data processing strategy based on the Driving Monitoring Dataset (DMD) is introduced to capture driver behavior from frontal, lateral, and hand views. To effectively model the temporal dynamics of driver behavior, a fatigue behavior monitoring network is designed using LSTM. Comparative experiments demonstrate that the proposed AlphaPose*-LSTM-based system achieves superior performance in fatigue detection tasks compared to existing state-of-the-art approaches.</p>

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A fatigue driving detection method based on driver posture and facial state analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

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  • Yuting Hao,
  • Xiuqian Sun,
  • Hao Liu,
  • Dapeng Wang

摘要

Fatigue driving is one of the primary causes of traffic accidents, posing a serious threat not only to the driver’s life and property but also to the safety of surrounding vehicles and pedestrians. Therefore, accurate and efficient detection of driver fatigue is essential for preventing traffic incidents. This study proposes a novel driver fatigue detection framework that integrates AlphaPose* with a Long Short-Term Memory (LSTM) network. To enhance the performance of AlphaPose, the original human detector is replaced with an optimized YOLOv11n model, which incorporates a Hybrid Pooling Fusion Block (HPFB) to improve feature representation and meet the requirements of keypoint estimation. Furthermore, a multi-view data processing strategy based on the Driving Monitoring Dataset (DMD) is introduced to capture driver behavior from frontal, lateral, and hand views. To effectively model the temporal dynamics of driver behavior, a fatigue behavior monitoring network is designed using LSTM. Comparative experiments demonstrate that the proposed AlphaPose*-LSTM-based system achieves superior performance in fatigue detection tasks compared to existing state-of-the-art approaches.