{
  "abstract": "Driver distraction is a major road-safety concern that requires reliable and efficient in-vehicle monitoring systems. The main contribution of this work is a reproducible driver-disjoint and deployment-oriented evaluation framework that jointly examines unseen-driver generalization, lightweight model benchmarking, explainability, calibration, and embedded inference. Experiments on the State Farm Distracted Driver Detection dataset show that MobileNetV3-Large provides the best trade-off among the evaluated lightweight models, achieving 88.92% test accuracy, 89.02% balanced accuracy, 88.07% macro-F1, and 97.88% Top-3 accuracy on unseen drivers. Explainable AI analysis indicates that the model mainly focuses on behavior-relevant regions, including the hands, face, phone area, steering wheel, and upper-body posture. For embedded deployment, TensorRT optimization on the Jetson Orin Nano Super achieved 212.94 FPS with 4.67 ms end-to-end latency in FP16 mode. These results demonstrate a practical balance between unseen-driver generalization, interpretability, and real-time embedded inference.",
  "abstract_shown": true,
  "affiliations": [
    "IRIS Team, MIASI Laboratory, Faculty of Applied Sciences, Nador, Mohammed Premier University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
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      "name": "Siham Essahraui"
    },
    {
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      ],
      "name": "Chaymae Rami"
    },
    {
      "affs": [
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      ],
      "name": "Khalid El Makkaoui"
    },
    {
      "affs": [
        0
      ],
      "name": "Ibrahim Ouahbi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bxny1bwt8pp6g.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62654-5",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62654-5",
  "id": "bxny1bwt8pp6g",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62654-5",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
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      "lcc",
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    [
      "openalex",
      "3207|Human-Automation Interaction and Safety"
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  "openalex": "W7169603100",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62654-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/bxny1bwt8pp6g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:56Z",
      "native_id": "1feb2fb3c746445e8258734cfd1b76d9",
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      "url": "https://doi.org/10.1038/s41598-026-62654-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "A lightweight deep learning model for real-time in-vehicle driver distraction detection with low-latency inference",
  "title_text": "A lightweight deep learning model for real-time in-vehicle driver distraction detection with low-latency inference",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:56Z",
  "url": "https://research.draconitic.com/paper/bxny1bwt8pp6g",
  "venue": "Scientific Reports",
  "versions": []
}