文章基本信息

标题：Driver Behavioral Classification on Curves Based on the Relationship between Speed, Trajectories, and Eye Movements: A Driving Simulator Study
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作者：Maria Emilia Schio Rondora ; Ali Pirdavani ; Ana Paula C. Larocca 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：10
页码：6241
DOI：10.3390/su14106241
语种：English
出版社：MDPI, Open Access Journal
摘要：Horizontal curves of rural highways are prone to a considerably high number of fatalities because an erroneous perception can lead to unsafe driving. This generally occurs when a driver fails to notice the highway geometry or changes in the driving environment, particularly curved segments. This study aimed to understand the geometric characteristics of curved segments, such as radius and approach tangents, on the driving performance towards minimizing vehicle crashes. Speed profiles and lateral position, the most common indicators of successful negotiation in curves, and eye movements were recorded during an experiment conducted in a fixed-base driving simulator equipped with an eye-tracking system with a road infrastructure (a three-lane highway) and its surroundings. A driving simulator can faithfully reproduce any situation and enable sustainable research because it is a high-tech and cost-effective tool allowing repeatability in a laboratory. The experiment was conducted with 28 drivers who covered approximately 500 test kilometers with 90 horizontal curves comprising nine different combinations of radii and approach tangent lengths. The drivers’ behavior on each curve was classified as ideal, normal, intermediate, cutting, or correcting according to their trajectories and speed changes for analyses of the performance parameters and their correlation conducted by factorial ANOVA and Pearson chi-square tests. The cross-tabulation results indicated that the safest behavior significantly increased when the curve radius increased, and the performance measures of curve radii were greatly affected. However, the driving behavior was not affected by the approach tangent length. The results revealed segments of the road that require a driver’s closer attention for essential vehicle control, critical information, and vehicle control in different parts of the task.