Improving risky driving behavior classification via conditional GAN-augmented hybrid machine learning models: a two-country naturalistic driving study, August 2026
A Paper titled “Improving risky driving behavior classification via conditional GAN-augmented hybrid machine learning models: a two-country naturalistic driving study” authored by Eleni Maria Theodoraki, Paraskevi Koliou and George Yannis has been published in European Transport Research Review. This Paper introduces a hybrid machine learning pipeline that combines Conditional Generative Adversarial Networks (cGANs) for creating realistic synthetic samples with robust classifiers. Furthermore, synthetic minority-class examples were generated with cGANs and combined with real training data. The analysis revealed that augmentation shifted feature importance distributions, reducing speed-metric dominance and increasing the relative importance of distance and physiological features, but whether these shifts reflect genuine generalisation improvements or synthetic data artefacts requires further validation. The results demonstrate how cGAN-based augmentation maintains minority-class detection performance under severe class imbalance while preserving interpretability through SHAP analysis, though further validation on independent driver populations is required before deployment in safety-critical contexts. ![]()





