Data To Safety Leveraging Deep Learning for Intelligent Driver Behavior Analysis

Road safety is a huge global problem, so understanding driver behavior is important for preventing accidents. This paper introduces a greatly improved method for analyzing complex driver behavior. This method uses advanced deep learning techniques, specifically Convolutional Neural Networks (CNN) and TensorFlow. We analyze important quantities of driving data to recognize trends and unusual events indicating unsafe driving. Training a CNN model on this dataset will allow us to achieve a high degree of accuracy in classifying, along with predicting, several driver actions. The proposed system processes data, giving drivers immediate feedback; in addition to this, it potentially alerts them to hazardous behaviors before accidents. Experimental results show our model’s superior performance compared to several other common methods. This points out the effectiveness of deep learning for improving road safety.