A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation
In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG...
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MDPI AG
2016-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/2/242 |
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doaj-c150bb8191e04ae687e9c71b7e42f9a42020-11-25T00:51:37ZengMDPI AGSensors1424-82202016-02-0116224210.3390/s16020242s16020242A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse RepresentationZutao Zhang0Dianyuan Luo1Yagubov Rasim2Yanjun Li3Guanjun Meng4Jian Xu5Chunbai Wang6School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Information Science & Technical, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Information Science & Technical, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Information Science & Technical, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaThe Psychological Research and Counseling Center, Southwest Jiaotong University, Chengdu 610031, ChinaThe Department of Industrial & Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USAIn this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.http://www.mdpi.com/1424-8220/16/2/242wearable electroencephalographicvigilance detectionvehicle active safetyvehicle speed controlsparse representationbrain-computer interface |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Zutao Zhang Dianyuan Luo Yagubov Rasim Yanjun Li Guanjun Meng Jian Xu Chunbai Wang |
| spellingShingle |
Zutao Zhang Dianyuan Luo Yagubov Rasim Yanjun Li Guanjun Meng Jian Xu Chunbai Wang A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation Sensors wearable electroencephalographic vigilance detection vehicle active safety vehicle speed control sparse representation brain-computer interface |
| author_facet |
Zutao Zhang Dianyuan Luo Yagubov Rasim Yanjun Li Guanjun Meng Jian Xu Chunbai Wang |
| author_sort |
Zutao Zhang |
| title |
A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation |
| title_short |
A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation |
| title_full |
A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation |
| title_fullStr |
A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation |
| title_full_unstemmed |
A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation |
| title_sort |
vehicle active safety model: vehicle speed control based on driver vigilance detection using wearable eeg and sparse representation |
| publisher |
MDPI AG |
| series |
Sensors |
| issn |
1424-8220 |
| publishDate |
2016-02-01 |
| description |
In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model. |
| topic |
wearable electroencephalographic vigilance detection vehicle active safety vehicle speed control sparse representation brain-computer interface |
| url |
http://www.mdpi.com/1424-8220/16/2/242 |
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