SGAP-Gaze fuses driver face, eye, and iris features with transformer attention over scene grids to estimate point-of-gaze, cutting mean pixel error by 23.5% versus prior models on a new urban driving dataset.
Real-time monitoring of driver distraction: State-of-the-art and future insights
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SHAP-selected features from multimodal physiological signals fed to a hybrid XGBoost-LightGBM ensemble yield 80.91% test accuracy and 0.79 macro-F1 for driving behavior classification.
citing papers explorer
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SGAP-Gaze: Scene Grid Attention Based Point-of-Gaze Estimation Network for Driver Gaze
SGAP-Gaze fuses driver face, eye, and iris features with transformer attention over scene grids to estimate point-of-gaze, cutting mean pixel error by 23.5% versus prior models on a new urban driving dataset.
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Physiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals
SHAP-selected features from multimodal physiological signals fed to a hybrid XGBoost-LightGBM ensemble yield 80.91% test accuracy and 0.79 macro-F1 for driving behavior classification.