Abstract
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.
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