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It is an important concept to\nprovide more safety and comfortability for the passengers during\nthe driving task. Researchers started to work on increasing\nautonomous vehicle detecting performance, by conducting\nsuitable solutions to overcome visibility constraints of the traffic\nsigns, such as, weather conditions, air pollution, color fade after\nlong exposure to sunlight and different types of obstacles. These\nsolutions represented by introducing several color spaces to have\npreferable traffic sign segmentation, for a data set of captured\nimages and videos. Afterwards, they have been processed to\nclassifier models to recognize and classify the categories of traffic\nsigns in an accurate manner. This paper summarizes the recent\nstudies on traffic signs detection and recognition by autonomous\nvehicles; it provides a comparison to the used methodologies and\napplications, and refers to their respective outreach and\nlimitations. 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