  With automobiles becoming increasingly reliant on sensors to perform various
driving tasks, it is important to encode the relevant CAN bus sensor data in a
way that captures the general state of the vehicle in a compact form. In this
paper, we develop a deep learning-based method, called Drive2Vec, for embedding
such sensor data in a low-dimensional yet actionable form. Our method is based
on stacked gated recurrent units (GRUs). It accepts a short interval of
automobile sensor data as input and computes a low-dimensional representation
of that data, which can then be used to accurately solve a range of tasks. With
this representation, we (1) predict the exact values of the sensors in the
short term (up to three seconds in the future), (2) forecast the long-term
average values of these same sensors, (3) infer additional contextual
information that is not encoded in the data, including the identity of the
driver behind the wheel, and (4) build a knowledge base that can be used to
auto-label data and identify risky states. We evaluate our approach on a
dataset collected by Audi, which equipped a fleet of test vehicles with data
loggers to store all sensor readings on 2,098 hours of driving on real roads.
We show in several experiments that our method outperforms other baselines by
up to 90%, and we further demonstrate how these embeddings of sensor data can
be used to solve a variety of real-world automotive applications.
