Driver Assistance System for Integration Interpretation of Driver’s Gaze and Selective Attention Model

This paper proposes a system to detect driver`s cognitive state by internal and external information of vehicle. The proposed system can measure driver`s eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver`s cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver`s characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver`s face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.