Cascaded Convolutional Neural Network with Attention Mechanism for Mobile EEGbased Driver Drowsiness Detection System The road accidents are a common cause to the injury and death of people. As reported by The American National Highway Traffic Safety Administration (NHTSA), the drivers drowsiness accounts for nearly 100,000 accidents per year in the United States. Thus, we present a novel drivers drowsiness detection system in this paper using the techniques of deep learning(DL), mobile computing, wearable device and Electroencephalography (EEG). We employ the deep learning architecture designed by ourselves that can be easily implemented on the mobile phone to detect the drowsiness with a high accuracy. The EEG signal we use is only single channel that can be easily obtained by the wearable device. The EEG signal collector is designed and made by ourselves, which is like a hair band that makes the driver easier and more comfortable to wear it. The whole system mainly consists of two parts: one is the hardware consisting of EEG headband and sensor, the other is software consisting of Android application and web platform. The app contains the fine trained model to make realtime prediction based on the EEG signal and alert the driver, while sending the data to the backend synchronously. The web platform provides an interface for the monitor to observe the condition of the driver. Our system achieved an accuracy of 97.09% detecting the drivers drowsiness, which surpasses the SOTA methods. The modelu0027s size and predict latency are also within a smaller scale than present models that make it more applicable to mobile and embedded system.

Attention Monitoring and Hazard Assessment with BioSensing and Vision: Empirical Analysis Utilizing CNNs on the KITTI Dataset Assessing the driveru0027s attention and detecting various hazardous and nonhazardous events during a drive are critical for driveru0027s safety. Attention monitoring in driving scenarios has mostly been carried out using vision (camerabased) modality by tracking the driveru0027s gaze and facial expressions. It is only recently that biosensing modalities such as Electroencephalogram (EEG) are being explored. But, there is another open problem which has not been explored sufficiently yet in this paradigm. This is the detection of specific events, hazardous and nonhazardous, during driving that affects the driveru0027s mental and physiological states. The other challenge in evaluating multimodal sensory applications is the absence of very large scale EEG data because of the various limitations of using EEG in the real world. In this paper, we use both of the above sensor modalities and compare them against the two tasks of assessing the driveru0027s attention and detecting hazardous vs. nonhazardous driving events. We collect user data on twelve subjects and show how in the absence of very largescale datasets, we can still use pretrained deep learning convolution networks to extract meaningful features from both of the above modalities. We used the publicly available KITTI dataset for evaluating our platform and to compare it with previous studies. Finally, we show that the results presented in this paper surpass the previous benchmark set up in the above driver awarenessrelated applications.