TripMD: Driving patterns investigation via Motif Analysis
This work addresses the need for better driving behavior analysis for applications like insurance and policy-making, but it is incremental as it builds on existing motif analysis techniques.
The authors tackled the problem of analyzing driving behavior by proposing TripMD, a system that extracts relevant driving patterns from sensor data and provides visualizations for investigation, demonstrating its ability to extract meaningful patterns from a single driver and identify unknown drivers from known sets.
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.