Distracted driving is a leading cause of road accidents. This risk is particularly critical during autonomous driving system takeover requests, where a driver's inattention can lead to severe consequences. A core challenge for existing detection methods lies in their limited adaptability to real‐world complex driving environments, such as variations across vehicle types and interference from multiple occupants. Specifically designed to address this issue, this paper proposes an enhanced detection framework based on real‐time detection transformer. We boost model performance via two core modules: the dynamic sparse gating multiscale attention module, which strengthens multiscale feature extraction through a dynamic sparse gating mechanism, and the attention‐guided dual‐path fusion module, which achieves precise cross‐layer feature fusion via dual–path interaction. Together, they significantly enhance the model's discriminative power and generalization capability in complex scenarios. Evaluation results on CBTDDD dataset demonstrate that the proposed method achieves a state‐of‐the‐art balance between accuracy and speed, with 97.1% mAP50 and 63.6 FPS. This represents a 2.5% mAP50 improvement over the baseline model and outperforms other mainstream lightweight models. Visualization analyses further confirm its superior attention focus. This research provides an effective pathway for promoting the practical application of distracted driving detection.
Liu et al. (2026) studied this question.