Physiological measurement based automatic driver cognitive distraction detection
thesisposted on 24.06.2013, 11:08 by Afizan Azman
Vehicle safety and road safety are two important issues. They are related to each other and road accidents are mostly caused by driver distraction. Issues related to driver distraction like eating, drinking, talking to a passenger, using IVIS (In-Vehicle Information System) and thinking something unrelated to driving are some of the main reasons for road accidents. Driver distraction can be categorized into 3 different types: visual distraction, manual distraction and cognitive distraction. Visual distraction is when driver's eyes are off the road and manual distraction is when the driver takes one or both hands off the steering wheel and places the hand/s on something that is not related to the driving safety. Cognitive distraction whereas happens when a driver's mind is not on the road. It has been found that cognitive distraction is the most dangerous among the three because the thinking process can induce a driver to view and/or handle something unrelated to the safety information while driving a vehicle. This study proposes a physiological measurement to detect driver cognitive distraction. Features like lips, eyebrows, mouth movement, eye movement, gaze rotation, head rotation and blinking frequency are used for the purpose. Three different sets of experiments were conducted. The first experiment was conducted in a lab with faceLAB cameras and served as a pilot study to determine the correlation between mouth movement and eye movement during cognitive distraction. The second experiment was conducted in a real traffic environment using faceAPI cameras to detect movement on lips and eyebrows. The third experiment was also conducted in a real traffic environment. However, both faceLAB and faceAPI toolkits were combined to capture more features. A reliable and stable classification algorithm called Dynamic Bayesian Network (DBN) was used as the main algorithm for analysis. A few more others algorithms like Support Vector Machine (SVM), Logistic Regression (LR), AdaBoost and Static Bayesian Network (SBN) were also used for comparison. Results showed that DBN is the best algorithm for driver cognitive distraction detection. Finally a comparison was also made to evaluate results from this study and those by other researchers. Experimental results showed that lips and eyebrows used in this study are strongly correlated and have a significant role in improving cognitive distraction detection.
Yayasan Telekom Malaysia (YTM)
- Computer Science