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STFTransNet: A Transformer Based Spatial Temporal Fusion Network for Enhanced Multimodal Driver Inattention State

Minjun Kim1, Gyuho Choi1

  • 1Department of Artificial Intelligence Engineering, Chosun University, 309, Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
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This study introduces a new AI model, STFTransNet, for recognizing driver inattention. It improves accuracy in challenging conditions like partial facial occlusion and poor lighting, enhancing road safety.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Driver inattention, including drowsiness and distraction, is a major cause of traffic accidents.
  • Existing driver inattention recognition systems struggle with challenges like partial facial occlusion and image degradation due to lighting variations.

Purpose of the Study:

  • To propose a novel transformer-based spatial-temporal fusion network (STFTransNet) for robust driver inattention state recognition.
  • To enhance the accuracy of driver state recognition in real-world driving scenarios with occluded faces and varying light conditions.

Main Methods:

  • Utilized mediapipe face mesh for facial landmark extraction.
  • Employed a two-stream cross-attention mechanism (RCN-based) for spatial feature learning from face and body action images.
Keywords:
STFTransNetdriver inattention state recognitionmultimodal drowsiness/distraction detectionpartial occlusiontransformer based spatial temporal fusion

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  • Implemented a temporal convolutional network (TCN-based) for temporal feature extraction.
  • Fused spatial and temporal features for final driver state classification.

    • Main Results:

    • The proposed STFTransNet achieved superior accuracy compared to existing models on multiple public datasets (NTHU-DDD, StateFarm, YawDD).
    • Demonstrated significant performance improvements of 4.56%, 3.48%, and 3.78% over baseline models on respective datasets.
    • Effectively addressed performance degradation caused by partial facial occlusion and light blur through multi-modality fusion.

    Conclusions:

    • STFTransNet offers a promising solution for advanced driver assistance systems, improving safety through accurate inattention detection.
    • The spatial-temporal fusion approach effectively handles challenging real-world driving conditions, paving the way for more reliable driver monitoring.