Leveraging Explainable Artificial Intelligence to Improve Advanced Driver Assistance Systems Through Driver Eye-Tracking Interpretation

Advanced Driver Assistance Systems (ADAS) enhance road safety by supporting drivers through warnings and control assistance; however, their effectiveness depends on accurate and interpretable recognition of driver behavior. This study proposes an ensemble learning framework that integrates deep learning and Explainable Artificial Intelligence (XAI) to classify driver behavioral states using eye-tracking data. Eye-region images are processed using ResNet50, DenseNet201, and InceptionV3 for feature extraction, and the extracted features are fused using an XGBoost classifier. The proposed framework achieves an overall classification accuracy of 94.90% and an average AUC of 0.97 across multiple gaze-related driving behavior classes. DenseNet201 contributes strong discrimination of fixation-related patterns, ResNet50 provides robust and generalizable spatial representations, and InceptionV3 captures multi-scale features associated with subtle gaze deviations. The ensemble model leverages these complementary representations to improve robustness and reduce misclassification. SHAP-based analysis revealed that upper and lateral gaze features positively contribute to attentive driving states, while lower-gaze regions, blink-related features, and pupil-related representations are the dominant contributors to distraction-related behaviors. These findings provide interpretable insight into how eye-tracking features drive model decisions. By combining quantitative performance gains with feature-level explanations, the proposed framework enables transparent, behavior-aware driver state monitoring and supports the development of adaptive and interpretable ADAS capable of informed intervention and control handover.