LAUSR

Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the MAX-MIN driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application.