Driver Identification Using Deep Generative Model With Limited Data

The scarcity of driving data constrains the accuracy of deep learning (DL)-based driver identification methods in practical application scenarios. To address this issue, this study proposes a novel unsupervised deep generative model called the convolution condition variant autoencoder (CCVAE) for driving data augmentation. In CCVAE, aided by driver identification information, the condition variant autoencoder can learn the real driving data distribution of each driver through an unsupervised learning paradigm; and aiming for better feature representation ability, convolutional neural network and deconvolution are leveraged, respectively. Therefore, a large number of synthetic samples can be generated by the generative part of the CCVAE. We demonstrate the effectiveness of the CCVAE through extensive experimental analysis using a real dataset collected from a vehicular CAN bus; the improvement of the DL-based driver identification results is demonstrated using synthetic samples. For instance, when only using 2% of the original data, approximately 20% improvement is achieved in terms of four evaluation indicators for two commonly used DL-based driver identification methods, namely, 1-D CNN and LSTM. Furthermore, several comparable experiments with state-of-the-art deep generative methods reveal the superior performance of the proposed CCVAE with respect to identification results, synthetic data quality, and model computation time. Therefore, the proposed model accomplishes a breakthrough in driver identification with limited data and shows great potential in data-driven applications of intelligent vehicles.