  In a society where traffic accidents frequently occur, fatigue driving has
emerged as a grave issue. Fatigue driving detection technology, especially
those based on the YOLOv8 deep learning model, has seen extensive research and
application as an effective preventive measure. This paper discusses in depth
the methods and technologies utilized in the YOLOv8 model to detect driver
fatigue, elaborates on the current research status both domestically and
internationally, and systematically introduces the processing methods and
algorithm principles for various datasets. This study aims to provide a robust
technical solution for preventing and detecting fatigue driving, thereby
contributing significantly to reducing traffic accidents and safeguarding
lives.
