Driver´s lane-change intent identification based on pupillary variation

In this paper, we propose a model to identify driver´s implicit intent based on eye movement analysis which is suitable for intelligent driver assistance system (IDAS). We use a lane-change intent-prediction system based on the human pupil size variation. Using the eye movement data as the input features, a discriminative classifier is trained to identify the probable lane-change maneuver at a particular point during the driving. In this paper we present the automated detection and recognition of lane-change intent based on driver´s pupillary variation. In the proposed method pupil size variation features are extracted using a glass-type eye-tracker.