预测(人工智能)
危害
心理学
计算机科学
认知心理学
人工智能
有机化学
化学
作者
Yisrael Parmet,Avinoam Borowsky,Omri Yona,Tal Oron-Gilad
出处
期刊:Human Factors
[SAGE]
日期:2014-09-02
卷期号:57 (2): 311-328
被引量:26
标识
DOI:10.1177/0018720814548220
摘要
In this study, we aimed to demonstrate analysis methods that are sensitive to speed-related differences between experienced and young novice drivers. These differences may be linked to determining which group is better at anticipating hazards.Awareness of hazardous situations, especially potential ones, is a major discriminator between experienced and young novice drivers who tend to misidentify potential hazards in the traffic environment.Experienced and young novice drivers were asked to drive a sequence of 14 scenarios in a driving simulator. Scenarios were created in two city areas, residential and business district, and included various types of hazards. Group homogeneity of speed for each group of drivers was computed for each scenario, and two business district scenarios were subjected to piecewise linear regression analysis.Group homogeneity analysis showed consistent and significant experience-based differences across all scenarios, revealing that the experienced drivers as a group were more homogenous in choosing their driving speed. Differences between groups were larger in the business district where speed was less restricted. Piecewise linear regression analysis revealed that experienced drivers approached uncontrolled intersections by slowing down and responded earlier to materialized events.Young novice drivers were more likely than experienced drivers to choose diverse values of speed at any given road section, presumably due to their poor awareness of potential and hidden hazards. Unlike other analysis methods, it is argued that group homogeneity of speed is a more sensitive measurement to reveal these gaps.Speed management could be the basis of future hazard anticipation simulator assessments.
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