An interpretable stacking ensemble learning model for visual-manual distraction level classification for in-vehicle interactions
Recognizing the level of driver distraction during the execution of secondary tasks within the intelligent cockpit is crucial for ensuring a seamless interaction between human drivers and intelligent vehicle systems. To address this issue, this paper proposes a framework for recognizing driver distraction levels that integrates clustering, classification, and interpretability. First, Feature Selection with Optimal Graph(SF2SOG) is employed to identify discriminative features from the data facilitating dimensionality reduction. Following this, Agglomerati...