Abstract
It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.
Highlights
Fatigue and alcohol have been identified as major factors in road accidents in many countries [1,2,3].Driver fatigue is involved in 10–15% of all severe crashes according to a conservative estimate [4].Alcohol consumption results in an annual death of 2.5 million people either from alcohol-related diseases or from accidents related to alcohol-impaired behavior [5].A lot of studies investigated the effects of fatigue and alcohol on driver’s performance
Fatigued driving and drunk driving behavior varied under different road geometries [17]
The results of analyses of variance (ANOVA) above showed some characteristic effects of different blood alcohol content (BAC) levels and fatigue on driving performance under different roadway geometries
Summary
Fatigue and alcohol have been identified as major factors in road accidents in many countries [1,2,3]. A lot of studies investigated the effects of fatigue and alcohol on driver’s performance. The previous study showed that different BAC levels and fatigue influence drivers’ driving. The previous study showed that different BAC levels and fatigue influence drivers’ driving performance, including SP_AVG, SP_SD, LP_AVG, LP_SD [17]. Fatigued driving and drunk driving behavior varied under different road geometries [17]. LP_AVG thetrends trendsof of effects effects imposed onon andand in in curves (dotted lines) were different from that in straight segments (solid lines). The curves (dotted lines) were different from that in straight segments (solid lines). The rangerange of variance caused by by alcohol and andLP_SD
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