Abstract
Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.
Highlights
Fatigue is a very complex physical and psychological state that can be divided into mental fatigue and physical fatigue
Research on fatigue driving identification methods mainly focuses on three aspects: (1) identification based on driver behavior characteristics: the driver’s fatigue state is judged by the recognition of the driver’s behavior, such as the movement of the eyelids, the closed state of the eyes [2], and facial expressions [3]. e identification method is simple and easy to implement, but the scoring standard is affected by conditions such as personal behavior, light, and image acquisition angle. e collection of various modal data will inevitably be noisy, causing the recognition result to fail to correctly identify the driver’s fatigue state
Reference [5] proposed a new real-time fatigue driving detection method based on EEG signals. e study combines two characteristics of power spectral density (PSD) and sample entropy (SampEn) to judge mental fatigue. e results show that the method is effective for fatigue detection because the prediction results of fatigue are consistent with the phenomena recorded in the simulated driving process. is is considered an objective measure of behavior
Summary
Fatigue is a very complex physical and psychological state that can be divided into mental fatigue and physical fatigue. Erefore, identifying driving fatigue states based on the EEG signals is considered to be one of the most objective and accurate analysis methods. Reference [5] proposed a new real-time fatigue driving detection method based on EEG signals. Reference [11] proposed the detection of the fatigue driving state based on the feature data of sample entropy, approximate entropy, and complexity, which can well identify four different mental fatigue states. Reference [21] detected fatigue driving by extracting the fatigue characteristics of blinking, slow eye movement, amplitude, and periodicity in the EOG signal, and the experimental results showed that the detection effect was effective. Is method can collect the driver’s EEG and EOG signals in a real environment and complete rapid identification and timely warning. When a driver is detected to be fatigued, the system sends a warning signal to the driver and informs other nearby vehicles using this system through the Internet of ings technology and manages the operation background
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