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As automotive electronics continue to advance, cars are becoming more andmore reliant on sensors to perform everyday driving operations. These sensorsare omnipresent and help the car navigate, reduce accidents, and providecomfortable rides. However, they can also be used to learn about the driversthemselves. In this paper, we propose a method to predict, from sensor datacollected at a single turn, the identity of a driver out of a given set ofindividuals. We cast the problem in terms of time series classification, whereour dataset contains sensor readings at one turn, repeated several times bymultiple drivers. We build a classifier to find unique patterns in eachindividual's driving style, which are visible in the data even on such a shortroad segment. To test our approach, we analyze a new dataset collected by AUDIAG and Audi Electronics Venture, where a fleet of test vehicles was equippedwith automotive data loggers storing all sensor readings on real roads. We showthat turns are particularly well-suited for detecting variations acrossdrivers, especially when compared to straightaways. We then focus on the 12most frequently made turns in the dataset, which include rural, urban, highwayon-ramps, and more, obtaining accurate identification results and learninguseful insights about driver behavior in a variety of settings.

Driver Identification Using Automobile Sensor Data from a Single Turn