TripMD: Driving patterns investigation via Motif Analysis

TL;DR

TripMD is a system that analyzes sensor data to extract and visualize driving patterns, aiding in understanding driver behavior and identifying individual driving styles, demonstrated on a public dataset.

Contribution

The paper introduces TripMD, a novel system for extracting and visualizing driving patterns from sensor data, enabling behavior analysis and driver identification.

Findings

Extracts meaningful driving patterns from sensor data.

Can identify driver behavior from a set of known drivers.

Effective on the UAH-DriveSet dataset.

Abstract

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.