TripMD: Driving patterns investigation via Motif Analysis
  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.
