Distracted driving behavior detection based on CNN and multi-model fusion

Abstract

text. In view of the low monitoring accuracy of drivers' dangerous behaviors, tired driving and distracted driving in traffic safety, and the inability to accurately and timely give early warning, in order to make-up for the shortcomings of existing detection methods and improve the accuracy of identification, in this paper, a novel distracted driving behavior detection method (CF-Net) based on CNN and multi-model fusion is proposed for intelligent cockpit environment. Firstly, a distracted driving detection model was built through the classification network, and the influences of different parameter values and classification networks on the model were analyzed on the open source data set of distracted driving. The XGBoost learning tool is added to the Stacking training mode to optimize the model. By comparing various models, the multi-model fusion network (CF-Net) distraction detection model is further applied to the driving environment, and the model's overall generalization ability and effectiveness are verified. The experimental results show that the detection performance based on a multi-model fusion network (CF-Net) is better, the test accuracy reaches 93.45%, the verification loss is reduced to the minimum, for the current intelligent cockpit environment, the driver distraction detection has a significant advantage.