A real-time driver fatigue identification method based on GA-GRNN

• Published in: Frontiers in public health Vol. 10; p. 991350
• Main Authors: Wang, Xiaoyuan, Chen, Longfei, Zhang, Yang, Shi, Huili, Wang, Gang, Wang, Quanzheng, Han, Junyan, Zhong, Fusheng
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 20.10.2022
• Subjects: active safety warning system; Algorithms; Automobile Driving; Cluster Analysis; Fatigue - diagnosis; fatigue driving; generalization regression neural network; genetic algorithm; Humans; machine vision; Neural Networks, Computer; Public Health; genetic algorithm; generalization regression neural network; active safety warning system; fatigue driving; machine vision
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2022.991350

It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.