Towards building reliable deep learning based driver identification systems

Recent studies have shown the potential of leveraging neural networks to achieve high levels of accuracy in re-identifying drivers by learning latent features from vehicular sensor data. However, deploying such networks in real-world applications (like theft detection or fleet management) requires re-training the networks with new data to transfer the learnings from the initial dataset to the target drivers. In this paper, we highlight the importance of the evaluation of such networks in both phases, initial training and transfer learning. Our evaluation shows that the performance of existing solutions drops significantly, when applied to new drivers that have not been seen by the networks in the initial training phase. Moreover, we propose a deep neural network that outperforms state-of-theart solutions in both phases. For the evaluation of the transfer learning phase, we use a dataset from a real-world ride-sharing service that has not been used in the initial training.