Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder

In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and long prediction time. We first propose using an unsupervised three-layer nonnegativity-constrained autoencoder to adaptive search the optimal size of the sliding window, then construct a deep nonnegativity-constrained autoencoder network to automatically extract hidden features of driving behavior to further complete driver identification. The results from the public driving behavior dataset indicate that relative to conventional sparse autoencoder, dropout-autoencoder, random tree, and random forest algorithms, our method can effectively search the optimal size of the sliding time window, and the window size is shortened from the traditional 60s to 30s, which can better preserve the intrinsic information of the data while greatly reducing the data volume. Furthermore, our method can extract more distinctive hidden features that aid the classifier to map out the separating boundaries among the classes more easily. Finally, our method can significantly shorten the prediction time and improve the timeliness under the premise of improving the driver identification performance and reducing the model overfitting. (C) 2018 Elsevier B.V. All rights reserved.