  Processing driving data and investigating driving behavior has been receiving
an increasing interest in the last decades, with applications ranging from car
insurance pricing to policy making. A common strategy to analyze driving
behavior is to study the maneuvers being performance by the driver. In this
paper, we propose TripMD, a system that extracts the most relevant driving
patterns from sensor recordings (such as acceleration) and provides a
visualization that allows for an easy investigation. Additionally, we test our
system using the UAH-DriveSet dataset, a publicly available naturalistic
driving dataset. We show that (1) our system can extract a rich number of
driving patterns from a single driver that are meaningful to understand driving
behaviors and (2) our system can be used to identify the driving behavior of an
unknown driver from a set of drivers whose behavior we know.
