Multinomial Logistic Regression Model for Predicting Driver＇s Drowsiness Using Only Behavioral Measures

The aim of this study was to explore the effectiveness of behavioral evaluation measures for predicting drivers＇ subjective drowsiness. Behavioral measures included neck bending angle, back pressure, foot pressure, COP （center of pressure） movement on sitting surface and tracking error in driving simulator task. Drowsy states were predicted by means of the multinomial logistic regression model where behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. First, we compared the effectiveness of two methods （correlation coefficient-based method and odds ratio-based method） for determining the order of entering behavioral measures into the prediction model. It was found that the prediction accuracy did not differ between both methods. Second, the prediction accuracy was compared among the numbers of behavioral measures. The prediction accuracy did not differ among four, five and six behavioral measures and it was concluded that entering at least four behavioral measures into the prediction model is enough to achieve higher prediction accuracy. Third, the prediction accuracy was compared between the strongly drowsy and the weakly drowsy groups. The prediction accuracy differed between the two groups and the proposed method was effective under the condition where drowsiness was induced to a larger extent.

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

A systematic review of the effect of various interventions on reducing fatigue and sleepiness while driving

Purpose： To identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving. Methods： This systematic review searched the following electronic databases： Medline, Science direct, Scopus, EMBASE, PsyclNFO, Transport Database, Cochrane, BIOSIS, ISI Web of Knowledge, specialist road injuries journals and the Australian Transport and Road Index database. Additional searches included websites of relevant organizations, reference lists of included studies, and issues of major injury journals published within the past 15 years. Studies were included if they investigated interventions/exposures accounting for reducing fatigue and sleepiness as the outcome, measured any potential interventions for mitigation of sleepiness and were written in English. Meta-analysis was not attempted because of the heterogeneity of the included studies. Results： Of 63 studies identified, 18 met the inclusion criteria. Based on results of our review, many interventions in the world have been used to reduce drowsiness while driving such as behavioral （talking to passengers, face washing, listening to the radio, no alcohol use, limiting the driving behavior at the time of 12 p.m. - 6 a.m. etc）, educational interventions and also changes in the environment （such as rumble strips, chevrons, variable message signs, etc）. Meta-analysis on the effect of all these in- terventions was impossible due to the high heterogeneity in methodology, effect size and interventions reported in the assessed studies. Conclusion： Results of present review showed various interventions in different parts of the world have been used to decrease drowsy driving. Although these interventions can be used in countries with high incidence of road traffic accidents, precise effect of each intervention is still unknown. Further studies are required for comparison of the efficiency of each intervention and localization of each intervention ac- cording to the traffic pattems of each country.

Driver Drowsiness Recognition Based on Computer Vision Technology

Driver drowsiness is one of the major causes of traffic accidents. This paper presents a nonintru- sive drowsiness recognition method using eye-tracking and image processing. A robust eye detection algo- rithm is introduced to address the problems caused by changes in illumination and driver posture. Six measures are calculated with percentage of eyelid closure, maximum closure duration, blink frequency, av- erage opening level of the eyes, opening velocity of the eyes, and closing velocity of the eyes. These meas- ures are combined using Fisher＇s linear discriminant functions using a stepwise method to reduce the cor- relations and extract an independent index. Results with six participants in driving simulator experiments demonstrate the feasibility of this video-based drowsiness recognition method that provided 86% accuracy.