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<p>Driver fatigue is a major contributor to traffic accidents, yet reliable early warning remains challenging under real-world and embedded constraints. This study presents a camera-only driver-fatigue detection pipeline that couples lightweight face tracking (MediaPipe) with a single Vision Transformer (ViT) classifier (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx\)</EquationSource> </InlineEquation>86M parameters) to infer alert vs. drowsy states from facial cues. The model is trained on a multi-source dataset of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\sim\)</EquationSource> </InlineEquation>4,000 images aggregated from seven public fatigue datasets and evaluated on a held-out test split, achieving 95.0% accuracy, 93.8% F1-score, and ROC-AUC of 0.986. On Raspberry Pi 5, end-to-end inference runs at 55.6 ms/frame (18.0 FPS). Compared with a detection-driven YOLO baseline using the same labeling protocol and test split, the proposed ViT improves accuracy (95.0% vs. 93.2%) and ROC-AUC (0.986 vs. 0.979) while trading throughput (18.0 vs. 28.0 FPS), clarifying the accuracy–latency balance for deployment. Real-world validation with 12 participants (22–47 years) across day and night sessions (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\sim\)</EquationSource> </InlineEquation>9 hours total) shows high agreement with manual annotations (92.8%), supporting operational feasibility. By combining a single-backbone ViT inference pipeline with explicit embedded benchmarking, matched baseline comparison, and in-vehicle validation, the proposed system provides a deployment-oriented evaluation package for camera-only fatigue monitoring under embedded ITS constraints.</p>

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Vision Transformer-Based Driver Fatigue Detection in Real-Time Embedded Systems

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Zahra Elmi,
  • Firdevs Eyidoğan,
  • Zeynep Sena Kaya,
  • Ali Buğuş,
  • Soheila Elmi

摘要

Driver fatigue is a major contributor to traffic accidents, yet reliable early warning remains challenging under real-world and embedded constraints. This study presents a camera-only driver-fatigue detection pipeline that couples lightweight face tracking (MediaPipe) with a single Vision Transformer (ViT) classifier ( \(\approx\) 86M parameters) to infer alert vs. drowsy states from facial cues. The model is trained on a multi-source dataset of \(\sim\) 4,000 images aggregated from seven public fatigue datasets and evaluated on a held-out test split, achieving 95.0% accuracy, 93.8% F1-score, and ROC-AUC of 0.986. On Raspberry Pi 5, end-to-end inference runs at 55.6 ms/frame (18.0 FPS). Compared with a detection-driven YOLO baseline using the same labeling protocol and test split, the proposed ViT improves accuracy (95.0% vs. 93.2%) and ROC-AUC (0.986 vs. 0.979) while trading throughput (18.0 vs. 28.0 FPS), clarifying the accuracy–latency balance for deployment. Real-world validation with 12 participants (22–47 years) across day and night sessions ( \(\sim\) 9 hours total) shows high agreement with manual annotations (92.8%), supporting operational feasibility. By combining a single-backbone ViT inference pipeline with explicit embedded benchmarking, matched baseline comparison, and in-vehicle validation, the proposed system provides a deployment-oriented evaluation package for camera-only fatigue monitoring under embedded ITS constraints.