Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 116, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2022.105399
Keywords
Intelligent transportation system; Driver fatigue detection; Abnormal behavior detection; Information fusion; RGB-D; Deep learning; Computer vision; Convolutional neural network
Categories
Funding
- Natural Science Foundation of Jiangsu Province, China [BK20191298]
- Science and Technology on Underwater Vehicle Technology Laboratory [2021JCJQ-SYSJJ-LB06905]
- Water Science and Technology Project of Jiangsu Province [20210722021063]
- Qinglan Project of Jiangsu Province
Ask authors/readers for more resources
This review summarizes the latest research findings and analyzes the developmental trends of driver fatigue detection. Four different fatigue detection technologies based on driver physiological signals, behavior features, vehicle running features, and information fusion are discussed. The applications of RGB-D camera and deep learning are highlighted. Experimental results showed the effectiveness of deep learning in extracting fatigue features.
Driver fatigue is an essential reason for traffic accidents, which poses a severe threat to people's lives and property. In this review, we summarize the latest research findings and analyze the developmental trends of driver fatigue detection. Firstly, we analyze and discuss four types of different fatigue detection technologies based on driver physiological signals, behavior features, vehicle running features, and information fusion, respectively. Then, we focus on RGB-D camera and deep learning which are two state-of-the-art solutions in this field. Finally, we present the work on integration of RGB-D camera and deep learning, where Generative Adversarial Networks and multi-channel schemes are utilized to enhance the performance. We conducted experiments to show that the fatigue features extracted by Convolutional Neural Networks are superior to traditional handcrafted ones while single features cannot guarantee robustness. Moreover, the latent fatigue features extracted by deep learning methods have been demonstrated to be effective for fatigue detection.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available