Low Parameter Neural Networks for In-Car Distracted Driver Detection

In response to the escalating global trend of increasing road accidents, particularly those attributed to distracted driving, this study endeavors to construct a precise and robust system for detecting distracted drivers and consequently issuing timely warnings. We propose a CNN-based framework designed not only to identify distracted drivers but also to determine the specific cause of the distraction. Experimental findings reveal that our system surpasses previous methodologies in the literature, achieving an accuracy of 99.31% with the ensemble method and 99.46% with our Convolution Neural Network. To ensure the practical applicability and adoption of our models within the car manufacturing industry, we designed them with a focus on minimizing the number of parameters. Furthermore, we present our architectures with a substantial reduction in the number of parameters from 140 million in the original VGG-16 to approximately 200 thousand.