Driver state estimation by convolutional neural network using multimodal sensor data

作者：Lim Sejoon^*; Yang Ji Hyun

来源：Electronics Letters, 2016, 52(17): 1495-1496.

DOI：10.1049/el.2016.1393

A driver state estimation algorithm that uses multimodal vehicular and physiological sensor data is proposed. Deep learning is applied to the fused multimodal data rather than each modality being treated as a different feature. A convolutional neural network model is developed and the driver state estimation algorithm is implemented using Google TensorFlow. The results show that deep learning is a very promising approach for driver state estimation compared with previously studied algorithms, such as dynamic Bayesian networks.

• 出版日期2016-8-18