Unobtrusive Driver Drowsiness Prediction Using Driving Behavior from Vehicular Sensors

Falling asleep is an eventual result of drowsiness, while driving it might also be a cause of major disasters. Driver's oblivious attempt of driving a vehicle when they are drowsy will lead to life-threatening accidents and even fatality. In this paper, we developed a framework to capture the drowsiness state of the driver using vehicle measures in an unobtrusive way. A well experimented VR-based simulated driving environment was employed to monitor the driver's drowsiness based on the acceleration, braking, and steering wheel axis pattern of the vehicle, in tandem with the self-estimated rating from the subject using KSS (Karolinska Sleepiness Scale). We proposed two prediction models to accomplish the drowsiness detection. We used the Classification as well the Regression techniques which produced a maximum accuracy rate of 99.10% and a minimum error rate of 0.34 RMSE. It was evaluated, based on its performance through the Ensemble classifier and Decision-Tree algorithms. As a result, it is identified that a system built using the Decision-Tree with the proposed segmentation of 4 sec window, could determine the driver's drowsiness at the earliest of 4.4 sec in the Classification and 4.5 sec with Regression, respectively.