We develop a deep neural network (DNN) for detecting driver drowsiness in videos. The proposed DNN model that receives driver's faces extracted from video frames as inputs consists of three components - a convolutional neural network (CNN), a convolutional control gate-based recurrent neural network (ConvCGRNN), and a voting layer. The CNN is to learn facial representations from global faces which are then fed to the ConvCGRNN to learn their temporal dependencies. The voting layer works like an ensemble of many sub-classifiers to predict drowsiness state. Experimental results on the NTHU-DDD dataset show that our model not only achieve a competitive accuracy of 84.81% without any post-processing but it can work in real-time with a high speed of about 100 fps.

We present a smart steering wheel that detects the gripping position and area, as well as the distance to the approaching driver's hands by measuring the resonant frequency and its resistance value in an LCR circuit composed of the floating capacitance between the gripping hand and the electrode of the steering, and the body resistance. The resonant frequency measurement provides a high sensitivity that enables the estimation of the distance to the approaching hand, the gripping area of a gloved hand, and for covering the steering surface with any type of insulating material. This system can be applied for drowsiness detection, driving technique improvements, and for customization of the driving settings.