Detection of drowsy driving based on driving information

Lee, Jeong-Woo; Lee, Shin-Kyung; Kim, Cheol-Hong; Kim, Kyong-Ho; Kwon, Oh-Cheon

doi:10.1109/ictc.2014.6983224

Cited by 9 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first category is not suitable for trains because they use a track [23]. The second category analyses the changes of driver behaviour, such as eye tracking, yawning, percent eye closure (PERCLOS), blink frequency, nodding frequency, facial position, and inclination of the driver’s head [24,25,26,27,28].…”

Section: Introductionmentioning

confidence: 99%

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

Zhang

Liu

et al. 2017

Sensors

125

View full text Add to dashboard Cite

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

show abstract

Section: Introductionmentioning

confidence: 99%

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

Zhang

Liu

et al. 2017

Sensors

125

View full text Add to dashboard Cite

show abstract

“…There are many methods [10–15] to detect driver's vigilance, of which three main types are: (i) vehicle‐behaviour‐based technology; (ii) driver‐physical‐based systems, and (iii) driver‐physiological change‐based systems.…”

Section: Introductionmentioning

confidence: 99%

Vigilance detection method for high‐speed rail using wireless wearable EEG collection technology based on low‐rank matrix decomposition

Zhou

Yao

Zhu

et al. 2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…The most popular approaches are classified into three categories [ 6 , 7 , 8 ]. The first category focuses on the movements of the vehicle [ 9 ], such as detecting the lane departure, steering wheel movement, the pressure of driving pedal. If the movement of the vehicle is abnormal, the driver is regarded as drowsy.…”

Section: Introductionmentioning

confidence: 99%

“…After the driver is drowsy, some proposed systems give warnings to the driver in order to avoid traffic accidents [ 9 , 15 , 17 ]. Despite warning of fatigue driving, most drivers believe they can drive safely.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Luo

Rasim

et al. 2016

Sensors

View full text Add to dashboard Cite

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.

show abstract

Detection of drowsy driving based on driving information

Cited by 9 publications

References 7 publications

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

Vigilance detection method for high‐speed rail using wireless wearable EEG collection technology based on low‐rank matrix decomposition

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

Contact Info

Product

Resources

About