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To prevent the severe consequences of unsafe driving behaviors, it is crucial to monitor and analyze the state of the driver. Developing an effective driver state monitoring (DSM) systems is particularly challenging due to limited computation capabilities of embedded systems in automobiles and the need for finishing processing in real-time. However, most of the existing research work was conducted in a lab environment with expensive equipment while lacking in-car benchmarking and validation. In this paper, a DSM system that estimates driver's alertness and drowsiness level as well as performs emotion detection built with a cost-effective embedded system is presented. The proposed system consists of a mono camera that captures driver's facial image in real-time and a machine learning based detection algorithm that detects facial landmark points and use that information to infer driver's state.

There is accumulating evidence that drowsy driving is one of the leading causes of vehicle crashes and accidents worldwide. Consequently, automotive manufacturers started to develop in-vehicle drowsiness detection devices. However, due to the limited computation resources and the complexity of the vehicular environment, the existing products' performance is limited. Moreover, the vast majority of the commercialized products focus on monitoring the subject's current drowsiness level, whereas predicting drowsiness level in advance to avoid future risks is overlooked. In this research, a multi-time-scale fusion approach is proposed where prediction results from both long-term and short-term Recurrent Neural Networks (RNN) were combined to predict a person's drowsiness level. Our results indicate that the proposed fusion strategies can successfully capture both the short-term microsleep-related features and long-term sleepiness features and improve the drowsiness prediction performance.