DrowsyFormer-Gen：经典的基线模型采用三流变压器与扩散增强技术进行驾驶员疲劳检测

Driver drowsiness detectors must be judged on minority-class sensitivity, calibration and alarm behaviour, not accuracy alone. DrowsyFormer-Gen combines physiological, vehicle and behavioural transformer streams with cross-modal fusion, an adaptive classification head and class-conditional diffusion augmentation. We re-implemented and evaluated it on shared causal windows and subject-disjoint folds against five classical baselines, using DMPS (225,000 samples; 15 drivers) and SC-DD (87,430 segments; 20 participants); DMPS drivers are a subset of SC-DD participants. Baselines received nested hy