  Cars can nowadays record several thousands of signals through the CAN bus
technology and potentially provide real-time information on the car, the driver
and the surrounding environment. This paper proposes a new method for the
analysis and classification of driver behavior using a selected subset of CAN
bus signals, specifically gas pedal position, brake pedal pressure, steering
wheel angle, steering wheel momentum, velocity, RPM, frontal and lateral
acceleration. Data has been collected in a completely uncontrolled experiment,
where 64 people drove 10 cars for or a total of over 2000 driving trips without
any type of pre-determined driving instruction on a wide variety of road
scenarios. We propose an unsupervised learning technique that clusters drivers
in different groups, and offers a validation method to test the robustness of
clustering in a wide range of experimental settings. The minimal amount of data
needed to preserve robust driver clustering is also computed. The presented
study provides a new methodology for near-real-time classification of driver
behavior in uncontrolled environments.
