Deep Learning Car Driver Motion Magnified Saccadic Eye Movements for Advanced Driving Assistance System

Automotive industry is making rapid progress in the development of next generation cars with higher levels of autonomy and intelligent assistance. Although the general advanced driver assistance system (ADAS) architecture is widely discussed, limited interaction between driver and these intelligent solutions sometimes make these approaches inefficient. For these reasons, the authors triggered an investigation about driver's feedback in relation to the assistance inputs provided by the ADAS technologies. In this context, the goal of this proposal is the design of an intelligent system that learns from the analysis of the car driver eyes saccadic movements, the correlated level of attention towards the salient driving scene. With this approach, we enabled a visual-feedback system which learns the driver eye's fixing dynamic associated to the analyzed driving scene. Through ad-hoc enhanced motion magnification technique, the authors were able to amplify the mentioned saccadic dynamics in order to allow a downstream deep classifier to associate this physiological behavior with the corresponding level of the driver attention. The collected performances (over 97%) confirmed the effectiveness of the proposed method.