Driver's emotion and behavior classification system based on Internet of Things and deep learning for Advanced Driver Assistance System (ADAS)

Classification of driver's emotion is an important issue that can be used to increase awareness of driving habits of drivers as many drivers are overconfident and are unaware of their bad driving habits. If the drivers driving behaviors are identified automatically, the drivers can be aware of their bad habits which can assist them to avoid potential car accidents. Many researches have suggested many methods or techniques that can be helpful in determining driver's behavior but there is no comprehensive method existed that can cater almost all types of distractions which occur while driving. In this paper the emotional as well as behavioral distraction states of the driver are analyzed and classified using advance deep learning algorithms like convolutional neural networks (CNN) and visual geometry group (VGG16). The handling of both the emotional and behavioral states can be helpful in developing a comprehensive driver's detection system in an efficient manner addressing the previous limitations or challenges that are needed to be solved. The results show that the accuracy of CNN was far better than that of VGG16 but the training time of VGG16 was far less than that of CNN This system can be further integrated with the in-vehicle driving systems or built-in safety systems.