Driver distraction is a leading factor in car crashes. With a goal to reduce traffic accidents and improve transportation safety, this study proposes a driver distraction detection system which identifies various types of distractions through a camera observing the driver. An assisted driving testbed is developed for the purpose of creating realistic driving experiences and validating the distraction detection algorithms. The authors collected a dataset which consists of images of the drivers in both normal and distracted driving postures. Four deep convolutional neural networks including VGG-16, AlexNet, GoogleNet, and residual network are implemented and evaluated on an embedded graphic processing unit platform. In addition, they developed a conversational warning system that alerts the driver in real-time when he/she does not focus on the driving task. Experimental results show that the proposed approach outperforms the baseline one which has only 256 neurons in the fully-connected layers. Furthermore, the results indicate that the GoogleNet is the best model out of the four for distraction detection in the driving simulator testbed.
Real-time detection of distracted driving based on deep learning
IET Intelligent Transport Systems ; 12 , 10 ; 1210-1219
2018-08-20
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
traffic accident reduction , conversational warning system , driver distraction detection system , real-time detection , car crashes , transportation safety , learning (artificial intelligence) , deep convolutional neural networks , neural nets , distraction detection algorithms , driving simulator testbed , road traffic , realistic driving experiences , distracted driving postures , deep learning , driver information systems , object detection , assisted driving testbed , driving task , road safety , GoogleNet , residual network , AlexNet , embedded graphic processing unit platform , fully-connected layers , VGG-16 , cameras , camera , graphics processing units , road accidents
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Europäisches Patentamt | 2019
|Europäisches Patentamt | 2019
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