Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies.

ABSTRACT: Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapi

SUBMITTER: Lin N 

PROVIDER: S-EPMC11236172 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature