title: A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.
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.
authors.affiliation: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. zzt@swjtu.edu.cn.
authors.forename: Zutao
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: School of Information Science &amp; Technical, Southwest Jiaotong University, Chengdu 610031, China. luodianyuan@my.swjtu.edu.cn.
authors.forename: Dianyuan
authors.initials: D
authors.lastname: Luo
authors.affiliation: School of Information Science &amp; Technical, Southwest Jiaotong University, Chengdu 610031, China. rasim_yagubov@hotmail.com.
authors.forename: Yagubov
authors.initials: Y
authors.lastname: Rasim
authors.affiliation: School of Information Science &amp; Technical, Southwest Jiaotong University, Chengdu 610031, China. liyanjun@my.swjtu.edu.cn.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. meng_guanjun@my.swjtu.edu.cn.
authors.forename: Guanjun
authors.initials: G
authors.lastname: Meng
authors.affiliation: The Psychological Research and Counseling Center, Southwest Jiaotong University, Chengdu 610031, China. xujianlm@gmail.com.
authors.forename: Jian
authors.initials: J
authors.lastname: Xu
authors.affiliation: The Department of Industrial &amp; Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA. chbwang@iastate.edu.
authors.forename: Chunbai
authors.initials: C
authors.lastname: Wang
mesh_terms: D000063:Accidents, Traffic; D000465:Algorithms; D001334:Automobile Driving; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D014584:User-Computer Interface; D014851:Wakefulness
keywords: brain-computer interface; sparse representation; vehicle active safety; vehicle speed control; vigilance detection; wearable electroencephalographic