Driver identification through vehicular CAN bus data: An ensemble deep learning approach

Driver identification using in-vehicle data is receiving considerable attention in the field of intelligent transportation owing to the advances in deep learning (DL). In order to improve accuracy and robustness of identification, this paper proposes an ensemble deep learning framework that integrates a modified one-dimensional convolutional neural network (M 1-D CNN) and bidirectional long short-term memory (BLSTM) to improve the performance and robustness of driver identification using information extracted from vehicular CAN-bus signals. The M 1-D CNN architecture is developed by adopting inception blocks, residual connection, and global average pooling to obtain optimal deep-feature representations of local time series. The BLSTM is used to learn the bidirectional long-term temporal dependencies. Results of extensive experiments using real driving data show that the proposed ensemble DL model can improve the accuracy and robustness of driver identification. Furthermore, four data augmentation methods, namely up-sampling, adding noise, data reversal, and random drifting, are used to expand the original training data to improve the performance of the ensemble method. Especially, few-shot learning is performed using the four data augmentation methods, and it shows excellent potential for driver identification with limited data.