Information Sources and Incremental Value in Short-Horizon Prediction of a Multimodal Driving Index in Extra-Long Tunnels

Predicting driver-state evolution in extra-long tunnel corridors remains challenging because of prolonged spatial confinement and repeated lighting transitions. This study uses a statistical human–vehicle composite, the comprehensive driving index (CDI), as a reproducible quantitative target for predictive auditing. In this study, multimodal information denotes synchronized ocular, physiological, vehicle-motion, and environmental sensor signals; the objective is to quantify their incremental predictive value rather than introduce a new fusion architecture. Fully nested leave-one-driver-out cross-validation with a prespecified 120 s unsupervised initialization estimated all preprocessing, scaling, PCA, model-selection, and calibration parameters from training data only. The five components explained 60.36% of target variance. In the original-range 30 s task (4835 evaluation windows), history-only ridge regression achieved an RMSE of 0.08294 and an R2 of 0.166, while directly tuned AR achieved an RMSE of 0.08261 and an R2 of 0.168. On the common 4259-window sample, expanded ridge and AR achieved RMSEs of 0.08123 (R2 0.187) and 0.08153 (R2 0.182). Adding coarse scene information produced an ΔRMSE = +0.00002 (95% CI −0.00026 to 0.00027), whereas external environmental summaries produced an ΔRMSE = −0.00019 (95% CI −0.00037 to −0.00003). HistGradientBoosting did not improve performance. The primary contribution is a leakage-controlled predictive-audit framework for screening candidate information sources before deployment decisions.