SAFETYLIT WEEKLY UPDATE

We compile citations and summaries of about 400 new articles every week.
RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article

Citation

Zhang Z, Luo D, Rasim Y, Li Y, Meng G, Xu J, Wang C. Sensors (Basel) 2016; 16(2): 242.

Affiliation

The Department of Industrial & Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA. chbwang@iastate.edu.

Copyright

(Copyright © 2016, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s16020242

PMID

26907278

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.



Keywords: Driver distraction;


Language: en

NEW SEARCH


All SafetyLit records are available for automatic download to Zotero & Mendeley
Print