The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate.
<p>In this paper, the implementation of ISO 9241- 210:2010 (Human Centred Design for Interactive Systems) standard for the development of a mobile application is presented in order to strengthen the user experience when using the mobile application in situ. Following the phases that the standard dictates for the development and evaluation of software and hardware in order to obtain a working prototype, and at the end of the process a product. The implementation of the standard allowed to generate an initial prototype validated by real users (tourists), so that, for future work will be carried out using artificial intelligence (AI) techniques and data analysis, these same, will complement this work, resulting in a fully validated and functional application for Smart Tourism. It should be noted that the purpose is to use User-Centered Design (UCD), thus achieving a high-fidelity prototype.</p>
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