Advancing Driver Behavior Recognition: An Intelligent Approach Utilizing ResNet

Abstract

In pursuit of enhancing public safety and addressing challenges in driver behavior recognition, an intelligent recognition and detection method of driver behavior based on ResNet (IRDMDB-ResNet) is proposed. The approach aims to identify instances of distracted driving resulting from abnormal behavior. Three models (IRDMDB-1, IRDMDB-2, and IRDMDB-3) are presented to implement this method, which is adapted to a deep learning behavior recognition in driving scenarios. Firstly, this study utilizes two well-tested real datasets: Driver Drowsiness Dataset and The State Farm. These datasets undergo preprocessing to meet the input requirements of the model. Secondly, a lightweight convolutional neural network model has been designed to extract features, aiding the warning system in delivering precise information and minimizing traffic collisions to the maximum extent possible. Finally, the model is evaluated based on the confusion metrics, accuracy, precision, recall, and F1-score criterion. As a result, the IRDMDB-3 model proposed in this paper can recognize and detect driver behavior effectively and stably. And it achieves 99.79% of accuracy in the classification of distracted drivers looking elsewhere in The State Farm dataset. Similarly, the detection at Driver Drowsiness Dataset is 99.68%. This advancement represents a significant improvement in traffic safety, showcasing adaptability to diverse behaviors and remarkable recognition and detection capabilities.