Driver Drowsiness Detection System (D3S)

posted Dec 21, 2010, 11:31 PM by Unknown user   [ updated Nov 1, 2013, 10:52 PM by Arpit ]
Self Initiated Project. 




Designed a real time drowsiness detection system where input parameters (Physiological: Eye blink rate, Eye blink time, Eye gaze. & Performance: Steering wheel angle, Accelerator & Brake pedal handling) are fed into a new Adaptive Neural Fuzzy Inference System algorithm which takes decisions about the drowsiness level of the driver with respect to the parameter's deviation from normal behavior. 


After the drowsiness level is predicted, a very specific alarm is generated which increases the alertness of the driver. This alarm is also called the brainwave which brings his/her drowsiness level out of dangerous levels. 


A video below shows the performance of the eye blink setup. 

Eye Blink Video



A flow chart of the algorithm is shown below - 




This project was awarded the "Innovation Award"  by the General Electric Company. 


Also, it was awarded a "Gold Medal" in the Annual International Project Presentation Competition held at BITS, Pilani. 


The Eye Blink Rate was calculated using IR sensors. Where closed eyes reflected incoming IR radiations, open eyes absorbed IR radiations. 


The Eye Blink Time was also calculated using IR sensors, calculating the time for which eyes were closed. 


Gaze direction was calculated using image processing algorithms which was used to alert the driver in case of abnormal gaze direction for a prolonged time. Also, algorithms were included to calculate the head drops both vertical and sideways. 


Performance Parameters like steering wheel angle, pressure on brake and accelerator pedals was used to characterize the state of human behavior. It was observed that when in tension (less consciousness) the peaks value of pressure on brake pedal was higher than during normal conditions. Also, the pressure on accelerator pedal had less amplitude, in such situations. 

The pressure on the brake and accelerator pedals was calculated using low cost potentiometers and a spring system, where pressure on springs made the potentiometers rotate. 

The steering wheel angle was also calculated using potentiometers and a mechanical setup. 

(Pictures of the above setup would be uploaded very soon). 


Emotional Activity could also be used for human behavior prediction. Where strong impulses are received when a person is undergoing stress. The two parameters which can be used are Heart Beat Rate & Skin Conductance. These parameters use ECG and EEG electrodes respectively but couldn't be interfaced in the device, due to lack of resources. 


The Fuzzy Filter puts all these parameters on a scale of 100. Where 100 denotes maximum drowsiness. These values are taken to form the training set (Human Adaptive) and are then used to compare with the real time input. The deviation from the training set values denotes drowsiness & a deviation above the threshold level becomes dangerous. 


Comments