Driver Identification Using Automobile Sensor Data from a Single Turn

TL;DR

This paper presents a novel method for identifying individual drivers based on sensor data collected during a single turn, demonstrating high accuracy across diverse driving scenarios using a new dataset.

Contribution

The study introduces a time series classification approach for driver identification from short sensor segments, validated on a new real-world dataset from Audi vehicles.

Findings

Turns provide distinctive driver signatures more than straightaways.

High accuracy achieved in identifying drivers across various turn types.

Sensor data can reveal individual driving styles even in brief road segments.

Abstract

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.