Research on Fatigue Driving State based on Multi-source Information Fusion

Abstract

To improve the accuracy and acceptance of driver fatigue recognition in practical applications and vehicle safety, by comparing the driver's face expression features, car driving characteristics and other multi-source fatigue information collection and driver acceptance, a fatigue driving state recognition scheme based on multi-source information fusion is proposed. The scheme includes two cameras and a steering angle sensor. The front-up camera collects eye position, frequency of blinks, frequency of glancing, staring time, and other indicators; The side camera collects the information of driver's head position; the steering angle sensor collects the steering wheel angle data. The fatigue driving state of the driver is classified and defined by a multi-source information detection method such as pupil characteristics, head tilt angle and steering wheel angle data. Through a comparison of Test Schemes for fatigue driving, it is concluded that the simulation driving experiment scheme has the best comprehensive index in terms of safety, economics, driving state fit and so on. Based on the work above, an experimental platform for fatigue driving recognition was established.