Road accidents due to distracted driving have been on a rise in recent years. As per the Road Accident Report 2019, 11 people were killed each hour in India due to road accidents (Transport Research Wing, 2020). This makes it crucial to take measures to stop the number of road fatalities. It found that the major cause of these accidents was driver error. This paper proposed a solution to detect the distraction of drivers into different predefined classes. The use of different pre-trained Convolutional Neural Network (CNN) models viz. AlexNet, VGG16, and ResNet50, for the classification of distracted drivers according to the State Farm’s Distracted Driver Detection challenge on Kaggle, are depicted in this paper, As well as MyModel is trained on Dataset consisting of different local driver’s 2D dashboard camera images along with the State Farm’s Dataset. After comparing the results of predefined models with MyModel, the best result has been found as a categorical cross-entropy loss of 0.2344 on the validation set and an accuracy score of 93.28%.

Sujay Jagadale

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Distracted Driver Detection: A Comparative Study using CNN.