![]() The results revealed that, in relation to the subjective assessment of fatigue, PERCLOS is a significant predictor of the changes observed in individual subjects during the performance of tasks, while ECD reflects the individual differences in subjective fatigue occurred both between drivers and in individual drivers between the ‘rested’ and ‘drowsy’ experimental conditions well. Three alternative models for subjective fatigue were used to analyse the relationship between the raw score of the FSS questionnaire, and the eye closure-associated indicators were estimated. The Fatigue Symptoms Scales (FSS) questionnaire was used to assess subjectively perceived levels of fatigue, whereas the percentage of eye closure time (PERCLOS), eye closure duration (ECD), and frequency of eye closure (FEC) were selected as eye closure-associated fatigue indicators, determined from the images of drivers’ faces captured by the sensor. The evaluation of the detector operation involved eight professional truck drivers, who drove the truck simulator twice-i.e., when they were rested and drowsy. This paper presents a camera-based prototype sensor for detecting fatigue and drowsiness in drivers, which are common causes of road accidents. Moreover, the latent fatigue features extracted by deep learning methods have been demonstrated to be effective for fatigue detection. 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. ![]() 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. Then, we focus on RGB-D camera and deep learning which are two state-of-the-art solutions in this field. 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. In this review, we summarize the latest research findings and analyze the developmental trends of driver fatigue detection. The outcome of this literature review could help practitioners to improve existing fatigue detection technologies by application of the different approaches for fatigue identification and measurement.ĭriver fatigue is an essential reason for traffic accidents, which poses a severe threat to people's lives and property. Analysis of papers shows that researchers are more likely to utilize a combination of physiological and behavior-based approaches to identify driving fatigue or drowsiness. Findings from the study indicate that physiological and behavior-based techniques are widely used by the authors, whereas vehicular features are very scarcely used. In the domain of DFD, researchers have used different approaches such as physiological, beha-vioral, vehicular, and mixed. This paper presents a systematic literature review of the research conducted over the last 15 years to provide information about the evolution of various driver fatigue detection (DFD) systems with the advancement of technologies. ![]() Therefore, a fatigue detection system is very important for safe driving. Fatigue impairs driving performance through a lack of concentration and slower reaction time. Moreover, the latent fatigue features extracted by deep learning methods have been demonstrated to be effective for fatigue detection.ĭriver fatigue is the most important factor in the increase in the frequency of traffic accidents and fatalities every year. Driver fatigue is an essential reason for traffic accidents, which poses a severe threat to people’s lives and property.
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