Prediction of Driver Head Movement via Bayesian Learning and ARMA Modeling

Abstract

This paper introduces a drowsiness scale which illustrates instantaneous overall predictions about observed anomalous driver behavior. Driver can be informed about her/his own driving conditions by the camera mounted inside of the vehicle. Data obtained from driver behavior by observation is not sufficient to make a correct decision about overall vehicle and driver state unless road and vehicle conditions are also considered. Various driver related observations are involved in the design of an observatory system in collaboration with external road sensory inputs. In our system, we propose a Bayesian learning method about driver awareness state in learning phase. An auto-regressive moving average (ARMA) model is devised to be the driver drowsiness predictor. A mean-square tracking error is measured in different head positions to determine the predictor's reliability and robustness under different illumination and conditions. An empirical set of plots is derived for the head positions corresponding to normal and drowsy driving conditions.