  Identifying driving styles is the task of analyzing the behavior of drivers
in order to capture variations that will serve to discriminate different
drivers from each other. This task has become a prerequisite for a variety of
applications, including usage-based insurance, driver coaching, driver action
prediction, and even in designing autonomous vehicles; because driving style
encodes essential information needed by these applications. In this paper, we
present a deep-neural-network architecture, we term D-CRNN, for building
high-fidelity representations for driving style, that combine the power of
convolutional neural networks (CNN) and recurrent neural networks (RNN). Using
CNN, we capture semantic patterns of driver behavior from trajectories (such as
a turn or a braking event). We then find temporal dependencies between these
semantic patterns using RNN to encode driving style. We demonstrate the
effectiveness of these techniques for driver identification by learning driving
style through extensive experiments conducted on several large, real-world
datasets, and comparing the results with the state-of-the-art deep-learning and
non-deep-learning solutions. These experiments also demonstrate a useful
example of bias removal, by presenting how we preprocess the input data by
sampling dissimilar trajectories for each driver to prevent spatial
memorization. Finally, this paper presents an analysis of the contribution of
different attributes for driver identification; we find that engine RPM, Speed,
and Acceleration are the best combination of features.
