The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth andWiFi and issue alarms of critical low driver attention levels. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented (2) A middleware architecture is proposed. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. 2008 bmw x5 front bumper WebDriving School 3D is the ultimate driving. The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. Free 3D Driving Simulator on Google Maps Tutorials d driving simulator google.
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