Abstract
Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.
Highlights
Among the three key factors: people, cars, and roads [3], the traffic accidents directly or indirectly caused by the “people” factor accounted for 92.9%, and the vast majority of traffic accidents are directly or indirectly related to the status of the driver [4]. e driver’s driving state directly affects the operating error rate and the ability to deal with emergencies. e fatigue that occurs during driving can cause the driver to become distracted and slow to react
Main Content and Innovation. e main content of this article is to study the respiratory physiological signals, driver operation signals, and eye signal detection in the fatigue driving detection, collect and extract the characteristics of the physiological signals and eye detection signals, and compare the signal fusion methods. e best decision-level fusion method is selected for the fusion of heterogeneous signals, and the high accuracy of the heterogeneous information fusion method in the detection of fatigue driving is confirmed by the detection of fatigue driving signals. e innovation of this paper is to combine the fatigue driving detection signal with heterogeneous signal fusion and achieve a high accuracy rate of fatigue driving detection by fusing the collected heterogeneous signals
The output weights of hidden nodes are usually learned in a single step, which is equivalent to learning a linear model [19]. ere will be no common problems such as falling into local minimum and overfitting in traditional neural networks, and it is suitable for modeling systems with complex nonlinear input and output relationships
Summary
To reduce the impact of fatigue on people, Wang proposed a method to provide real-time fatigue detection [5] He uses an active shape model to detect human faces and extracts the histogram features of directional gradients of the eyes and mouth. Li et al explored a feature weight-driven signal fusion method [6] and proposed interactive mutual information modeling to improve the accuracy of mental workload classification. E model can use various physiological and detection information to estimate driver fatigue in a probabilistic manner. E main content of this article is to study the respiratory physiological signals, driver operation signals, and eye signal detection in the fatigue driving detection, collect and extract the characteristics of the physiological signals and eye detection signals, and compare the signal fusion methods. Main Content and Innovation. e main content of this article is to study the respiratory physiological signals, driver operation signals, and eye signal detection in the fatigue driving detection, collect and extract the characteristics of the physiological signals and eye detection signals, and compare the signal fusion methods. e best decision-level fusion method is selected for the fusion of heterogeneous signals, and the high accuracy of the heterogeneous information fusion method in the detection of fatigue driving is confirmed by the detection of fatigue driving signals. e innovation of this paper is to combine the fatigue driving detection signal with heterogeneous signal fusion and achieve a high accuracy rate of fatigue driving detection by fusing the collected heterogeneous signals
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