IntroductionWhether rain, snow, and fog elicit qualitatively different driver-state dynamics remains unclear.MethodsThirty licensed drivers completed a simulator experiment under clear, rain, snow, and fog conditions, during which subjective scales, electrocardiograms, electroencephalograms, and vehicle data were acquired simultaneously. Six response pathways were constructed to characterize subjective load, operational fluctuation, conservative control, physiological arousal, electroencephalographic response, and system uncertainty. C4 spectral entropy gauge neural complexity, class-wise optimized reliability fusion prediction entropy measured system uncertainty in multimodal sensor responses, and within-subject centering, together with distance correlation, revealed how information is structured across modalities.ResultsAmong out-of-fold predictions over 6,456 windows, the class-wise optimized reliability fusion model achieved 91.9% accuracy and Macro-F1 of 0.919. All three adverse weather types significantly increased the subjective load, yet the dominant pathways differed. Rainfall increased cognitive load, snow led to synchronized increases in neural complexity and system uncertainty, and fog induced conservative compensation under visual restriction. Snow produced the strongest system-level response [class-wise optimized reliability fusion prediction entropy: r = 0.79, 95% confidence interval (CI): (0.62, 0.87); C4 spectral entropy: r = 0.53, 95% CI (0.21, 0.79)]. The four modalities formed an information-complementary structure.DiscussionThis study elucidates the information dynamics of driving states under adverse weather conditions and demonstrates that rain, snow, and fog occupy distinct regions within the information space, thereby providing a sensor-informed foundation for driver-state monitoring and graded warning systems in intelligent vehicles.