Abstract:
Driving under the influence of fatigue often results in uncontrollable vehicle dynamics, which causes severe and fatal
accidents. Therefore, early warning on the fatigue onset is crucial to avoid occurrences of such kind of a disaster. In
this paper, the authors have investigated a novel semi-supervised convolutional variational autoencoder-based
classification approach to classify the state of the driver. A convolutional variational autoencoder is a generative
network. The authors have proposed a discriminative model using convolutional variational autoencoders and
residual learning. This approach calculates an intermediate loss base on deep features of the network in addition to the
label information in training. The loss obtained by this method helps the training to be more effective on the model
and leads to better accuracy in driver fatigue classification. The trained model has managed to classify driver fatigue
with higher accuracy (97%) than the other successful models taken into comparison, proving that the proposed method
is more practical for computing classification loss for driver fatigue to currently available methods.
