  As automotive electronics continue to advance, cars are becoming more and
more reliant on sensors to perform everyday driving operations. These sensors
are omnipresent and help the car navigate, reduce accidents, and provide
comfortable rides. However, they can also be used to learn about the drivers
themselves. In this paper, we propose a method to predict, from sensor data
collected at a single turn, the identity of a driver out of a given set of
individuals. We cast the problem in terms of time series classification, where
our dataset contains sensor readings at one turn, repeated several times by
multiple drivers. We build a classifier to find unique patterns in each
individual's driving style, which are visible in the data even on such a short
road segment. To test our approach, we analyze a new dataset collected by AUDI
AG and Audi Electronics Venture, where a fleet of test vehicles was equipped
with automotive data loggers storing all sensor readings on real roads. We show
that turns are particularly well-suited for detecting variations across
drivers, especially when compared to straightaways. We then focus on the 12
most frequently made turns in the dataset, which include rural, urban, highway
on-ramps, and more, obtaining accurate identification results and learning
useful insights about driver behavior in a variety of settings.
