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<p>Drunk driving poses a serious risk to road safety. This study introduces a semi-supervised approach to detect intoxicated drivers by combining Variational Autoencoders (VAEs) for feature extraction with a One-Class Support Vector Machine (OCSVM) for anomaly detection. The method is trained only on data from sober drivers, making it suitable for limited-label scenarios. Model predictions are explained using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), which consistently identify alcohol concentration as the most influential feature. The proposed framework is evaluated using a publicly available dataset from IEEE Dataport, which includes sensor readings from alcohol gas sensors, facial temperature data, and pupil measurements captured by a Raspberry Pi camera. The VAE-OCSVM model achieves strong results, with an F1-score of 98%, outperforming standard clustering-based methods as well as classical semi-supervised statistical monitoring approaches, including Principal Component Analysis (PCA)-based Hotelling <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(T^2\)</EquationSource></InlineEquation>, PCA-based Squared Prediction Error (SPE), and Independent Component Analysis (ICA)-based SPE. Bootstrap-based confidence intervals are computed to assess the robustness of performance metrics.</p>

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Intelligent detection of alcohol-impaired driving via variational autoencoders and SHAP-based interpretation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Fouzi Harrou,
  • Abdelkader Dairi,
  • Abdelhakim Dorbane,
  • Ying Sun

摘要

Drunk driving poses a serious risk to road safety. This study introduces a semi-supervised approach to detect intoxicated drivers by combining Variational Autoencoders (VAEs) for feature extraction with a One-Class Support Vector Machine (OCSVM) for anomaly detection. The method is trained only on data from sober drivers, making it suitable for limited-label scenarios. Model predictions are explained using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), which consistently identify alcohol concentration as the most influential feature. The proposed framework is evaluated using a publicly available dataset from IEEE Dataport, which includes sensor readings from alcohol gas sensors, facial temperature data, and pupil measurements captured by a Raspberry Pi camera. The VAE-OCSVM model achieves strong results, with an F1-score of 98%, outperforming standard clustering-based methods as well as classical semi-supervised statistical monitoring approaches, including Principal Component Analysis (PCA)-based Hotelling \(T^2\), PCA-based Squared Prediction Error (SPE), and Independent Component Analysis (ICA)-based SPE. Bootstrap-based confidence intervals are computed to assess the robustness of performance metrics.