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- W651285926 abstract "Driver’s low vigilance is a significant factor contributing to traffic accidents. Numerous methods were proposed to measure driver’s alertness level and warn driver when a potential risk exists. These techniques commonly detect driver’s vigilance from three different aspects, including physiological signal indicators, vehicle steering behavior, and facial inattention features. However, it is difficult to estimate driver’s distraction or drowsiness accurately from the observation of single aspect. This paper presents an integrated method which non-intrusively detects driver’s low alertness by considering both the performance of vehicle steering and the inattention facial features. The authors characterize driving performance by analyzing the manipulation behavior of steering wheel. A new image processing algorithm using Sobel operator is designed to estimate driver’s gaze direction efficiently. The analysis results of these two different aspects are generally complementary and thus can be integrated to obtain accurate detection of driver’s inattention. The proposed method continually estimates the parameters of vehicle steering model and thus it is adaptive to the variety of driving behavior across time and individuals. Conducted experiments showed the proposed technique achieved high true positive rate (>99%) with low false positive error (0.12 times per hour)." @default.
- W651285926 created "2016-06-24" @default.
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- W651285926 date "2013-01-01" @default.
- W651285926 modified "2023-09-23" @default.
- W651285926 title "Detection of Driver’s Low Vigilance Using Vehicle Steering Information and Facial Inattention Features" @default.
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