Open AccessDriver state estimation by convolutional neural network using multimodal sensor data
Author(s) -
Lim Sejoon,
Yang Ji Hyun
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.1393
Subject(s) - convolutional neural network , computer science , artificial intelligence , deep learning , feature (linguistics) , state (computer science) , modality (human–computer interaction) , machine learning , estimation , pattern recognition (psychology) , artificial neural network , dynamic bayesian network , bayesian probability , algorithm , engineering , systems engineering , philosophy , linguistics
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.