Recongnition of Distracted Driving Behavior Based on Improved Bi-LSTM Model and attention mechanism

Distracted driving, a leading cause of traffic accidents with severe consequences, still faces numerous technical challenges in practical implementation for recognizing unsafe driving behavior. These challenges include the complexity of feature extraction using traditional convolutional neural networks (CNNs) for driver behavior analysis and the lack of real-time perception during driving. To address these issues, this study proposes an improved method for distracted driving behavior recognition by combining the Bi-LSTM model with an attention mechanism based on Dilated Convolutional Neural Networks (ID-CNN). Firstly, we employ a dilated convolution model to extract features efficiently with fewer parameters while enhancing multi-scale feature extraction capabilities and widening the receptive field. Subsequently, we integrate the attention mechanism into the Bi-LSTM model to enhance its effectiveness in solving the driving behavior classification problem. The integrated Bi-LSTM model with attention mechanism calculates correlation between intermediate and final states to obtain a probability distribution of attention weights at each moment, thereby reducing information redundancy while preserving useful information effectively. Furthermore, image feature vectors are enhanced to further improve accuracy in image classification tasks. Compared to other methods, the proposed approach exhibits faster convergence rates and more stable model accuracy. Specifically, on both the StateFarm dataset and our own collected Drive&Act-Distracted data, we achieved accuracies of 95.8367% and 97.8911%, respectively. This indicates that incorporating dilated convolution and attention mechanisms strengthens sequence data learning and feature weighting within our network model, resulting in significantly improved accuracy for driving behavior recognition.