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- W2897078774 abstract "Distracted driving is the main cause for car accidents. Driver inattention monitoring systems are promising solutions to mitigate this problem. In this paper, we propose a novel driver inattention monitoring system utilizing deep learning and fuzzy logic theory. A driver head pose estimation module is able to determine whether the driver is focusing on his frontal view, while a deep-learning-based distraction recognition module would detect whether the driver is performing a distraction activity. A danger level inference module based on fuzzy logic combines information from the head pose estimation and the distraction recognition modules to infer the danger level in a real-time manner. In the experimental work, a Convolutional Neural Network model is trained on data of high diversity allowing a more robust driver distraction detection compared to the model trained with only data collected by simulation experiments. In addition, we show that the proposed danger level inference strategy is an effective solution to detect dangerous driving situations by providing timely alerts depending on the vehicle speed." @default.
- W2897078774 created "2018-10-26" @default.
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- W2897078774 date "2018-07-01" @default.
- W2897078774 modified "2023-09-23" @default.
- W2897078774 title "Driver Behavior Monitoring Using Tools of Deep Learning and Fuzzy Inferencing" @default.
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- W2897078774 doi "https://doi.org/10.1109/fuzz-ieee.2018.8491511" @default.
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