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Sensors 2016, 16(2), 242; doi:10.3390/s16020242

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

1
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
School of Information Science & Technical, Southwest Jiaotong University, Chengdu 610031, China
3
The Psychological Research and Counseling Center, Southwest Jiaotong University, Chengdu 610031, China
4
The Department of Industrial & Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 4 January 2016 / Revised: 6 February 2016 / Accepted: 12 February 2016 / Published: 19 February 2016
(This article belongs to the Special Issue Sensors in New Road Vehicles)

Abstract

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. View Full-Text
Keywords: wearable electroencephalographic; vigilance detection; vehicle active safety; vehicle speed control; sparse representation; brain-computer interface wearable electroencephalographic; vigilance detection; vehicle active safety; vehicle speed control; sparse representation; brain-computer interface
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, Z.; Luo, D.; Rasim, Y.; Li, Y.; Meng, G.; Xu, J.; Wang, C. A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation. Sensors 2016, 16, 242.

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