Intelligent Deep Motion Magnification Analysis in Advanced Driving Assistance Systems

Automotive domain is making rapid growth in next generation cars development embedding higher levels of autonomy and intelligent assistance. Although the general advanced driver assistance system (ADAS) architectures are widely debated in the global automotive market, limited interaction between driver and these intelligent solutions sometimes make these solutions inefficient. For these reasons, the authors started an investigation about driver’s feedback with respect to the intelligent assistance inputs provided by the ADAS technologies. In this context, the goal of this proposal is to show the implemented intelligent system which learns from the analysis of the car driver’s eyes saccadic movements, the correlated level of attention towards the salient driving scene. With this approach, the authors were able to collect a kind of visual-feedback signal 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 to allow a downstream deep classifier to associate this physiological behavior with the corresponding level of the driver attention. The collected performances (near to 97