Use of new communication technologies in the behavioral and cognitive awareness of road userstowards safe driving

Résumé

Resumen de la tesis: In the past decades, there have been numerous advancements in the field of technology. Since the world¿s problems are increasing in complexity as it progresses, it is imperative that advances in science and technology be made in areas such as automatic recognition and detection. One of these problems is mental fatigue, which contributes to many accidents around the world. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Using machine learning and deep learning, we provide a different method to comprehend the meaning of fatigue, its detrimental impacts, as well as strategies to detect fatigue. The thesis also discusses classifier performance measures and comparison analyses with different automatic detection using the EEG signals. In fact the fatigue detection requires a lot of analysis, and especially the analysis of EEG signals, these signals are difficult to provide and very expensive, and depend on several parameters and many conditions, the first part of this thesis was very difficult since we made a lot of time to find our volunteers and to succeed in this experience, for the part of the analysis using the different algorithms of machine learning and deep learning and thus the comparison of the accuracy of each method. http://repositorio.bib.upct.es/dspace/