Driver stress detection via multimodal fusion using attention-based CNN-LSTM

摘要

Stress has been identified as one of major contributing factors in car crashes due to its negative impact on driving performance. It is in urgent need that the stress levels of drivers can be detected in real time with high accuracy so that intervening or navigating measures can be taken in time to mitigate the situation. Existing driver stress detection models mainly rely on traditional machine learning techniques to fuse multimodal data. However, due to the non-linear correlations among modalities, it is still challenging for traditional multimodal fusion methods to handle the real-time influx of complex multimodal and high dimensional data, and report drivers? stress levels accurately. To solve this issue, a framework of driver stress detection through multimodal fusion using attention based deep learning techniques is proposed in this paper. Specifically, an attention based convolutional neural networks (CNN) and long short-term memory (LSTM) model is proposed to fuse non-invasive data, including eye data, vehicle data, and environmental data. Then, the proposed model can automatically extract features separately from each modality and give different levels of attention to features from different modalities through self-attention mechanism. To verify the validity of the proposed method, extensive experiments have been carried out on our dataset collected using an advanced driving simulator. Experimental results demonstrate that the performance of the proposed method on driver stress detection outperforms the state-of-the-art models with an average accuracy of 95.5%.