An Attempt to Predict Point in Time with High Risk of Accident by Trend Analysis-Method for Detecting Significant Trend of Change of Behavioral Measures-

Abstract

From the practical viewpoint, only behavioral measures (in this study, eight behavioral measures) were used for drowsiness prediction. A variety of baseline of drowsiness (arousal state) was used in this study. More concretely, each behavioral measure was used as the base line of drowsiness (arousal state) as well as the self-reported evaluation of drowsiness, and thus we made an attempt to predict the participant’s drowsiness for each base line. Trend analysis of each evaluation measure was carried out by using a single regression model where time and base line of drowsiness (one of evaluation measures) corresponded to an independent variable and a dependent variable, respectively. Using the result of trend analysis, we proposed a new approach to predict the point in time (we call this the point in time of virtual accident) when the participant would have encountered a crucial accident if he was driving a car. On the basis of results of all participants, the proposed approach could identify the point in time of virtual accident, and was promising for identifying and predicting the time zone with potentially high risk (probability) of inducing an accident due to drowsy driving in advance, and for warning drivers of such a state.