  Drowsy driving represents a major contributor to traffic accidents, and the
implementation of driver drowsy driving detection systems has been proven to
significantly reduce the occurrence of such accidents. Despite the development
of numerous drowsy driving detection algorithms, many of them impose specific
prerequisites such as the availability of complete facial images, optimal
lighting conditions, and the use of RGB images. In our study, we introduce a
novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model
(MAF). MAF is aimed at significantly enhancing classification performance,
especially in scenarios involving partial facial occlusion and low lighting
conditions. It accomplishes this by capitalizing on the local feature
extraction capabilities provided by multi-attention fusion, thereby enhancing
the algorithm's overall robustness. To enhance our dataset, we collected
real-world data that includes both occluded and unoccluded faces captured under
nighttime and daytime lighting conditions. We conducted a comprehensive series
of experiments using both publicly available datasets and our self-built data.
The results of these experiments demonstrate that our proposed model achieves
an impressive driver drowsiness detection accuracy of 96.8%.
