Light-weight Convolutional Neural Network for Distracted Driver Classification

Driving is an activity that requires the coordination of many senses with complex manipulations. However, the driver can be affected by a several factors such as using a mobile phone, adjusting audio equipment, smoking, drinking, eating, talking to a passenger or drowsy. Therefore, the development of assistant applications to warn distracted driver is very necessary. Because of the limited space and mobility, the equipment also requires compact, energy-saving and efficient. This paper proposes a lightweight Convolutional Neural Network for a distracted driver warning system. The method is built based on a combination of standard convolution and Depthwise Separable Convolution operation to optimize the network parameters but still ensure the important information and speed. The network was trained and evaluated on two datasets, AUC (the American University in Cairo) and StateFarm dataset from Kaggle's competition. As a result, the evaluation accuracy reached 95.36% and 99.95%, respectively.

Automated summary

Driving is a complex activity that can be easily affected by various distractions. Therefore, developing assistant applications to warn distracted drivers is necessary. This paper proposes a lightweight Convolutional Neural Network for a distracted driver warning system, achieving accuracy rates of 95.36% and 99.95% on different datasets.