  With increasing focus on privacy protection, alternative methods to identify
vehicle operator without the use of biometric identifiers have gained traction
for automotive data analysis. The wide variety of sensors installed on modern
vehicles enable autonomous driving, reduce accidents and improve vehicle
handling. On the other hand, the data these sensors collect reflect drivers'
habit. Drivers' use of turn indicators, following distance, rate of
acceleration, etc. can be transformed to an embedding that is representative of
their behavior and identity. In this paper, we develop a deep learning
architecture (Driver2vec) to map a short interval of driving data into an
embedding space that represents the driver's behavior to assist in driver
identification. We develop a custom model that leverages performance gains of
temporal convolutional networks, embedding separation power of triplet loss and
classification accuracy of gradient boosting decision trees. Trained on a
dataset of 51 drivers provided by Nervtech, Driver2vec is able to accurately
identify the driver from a short 10-second interval of sensor data, achieving
an average pairwise driver identification accuracy of 83.1% from this 10-second
interval, which is remarkably higher than performance obtained in previous
studies. We then analyzed performance of Driver2vec to show that its
performance is consistent across scenarios and that modeling choices are sound.
