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Fatigue driver detection system using a combination of blinking rate and driving inactivity

For driving behavior, we acquired the vehicle’s state from inertial measurement unit (IMU) and gas pedal sensors. The principle component analysis (PCA) was used to select the components that have high variance. The variance values were used to differentiate fatigue drivers, which are assumed to have higher driving activities, from normal drivers. | Journal of Automation and Control Engineering Vol. 4, No. 1, February 2016 Fatigue Driver Detection System Using a Combination of Blinking Rate and Driving Inactivity Wasan Tansakul and Poj Tangamchit Control System and Instrumentation Engineering Department King Mongkut’s University of Technology Thonburi, Bangkok, Thailand Email: wasan.tans@gmail.com, ipojchit@kmutt.ac.th from the driver’s eyes. Eye behavior contains a useful clue for drowsiness. There are two approaches for detecting eye clues: Active and passive approaches. The active approach uses infrared light shining toward the eyes and detecting reflection. The passive approach relies on ambient light and detects eyes’ behavior. The drawback for the active light is that the light source, although infrared, has to be strong so that its reflection is clearly visible. This will create eye strain when using it on the driver’s eyes for a long period of time. Our work, on the other hand, chose the passive approach, which use ambient light or gentle light source. We use eye detection and tracking algorithm to detect blinking rate and duration. Fatigue drivers will have high blinking rate and longer duration than normal drivers. For driving behavior indicator, we install a 9-DOF inertial measurement unit (IMU) together with a gas pedal sensor. These sensors are used to measure the level of driving activity. The assumption we used is that fatigue drivers will have low level of activity, which will be reflected by the smoothness of sensor values. Since, there are many features from the sensors, we implement the principle component analysis (PCA) to reduce the dimension of data. Then, we measure the fluctuation of data by using the standard deviation to differentiate between normal drivers and fatigue drivers. Abstract—We implemented a fatigue driver detection system using a combination of driver’s state and driving behavior indicators. For driver’s state, the system monitored the eyes’ blinking rate and the .