TripMD: Driving patterns investigation via motif analysis. (1st December 2021)

• Record Type: Journal Article
• Authors: Silva, Maria Inês; Henriques, Roberto
• Is Part Of: Expert systems with applications. Volume 184(2021)
• Publication Date: 2021-12-01
• Subjects: Driving behaviors -- Road safety -- Motif discovery -- Acceleration -- Sensors -- Time-series; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115527
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Highlights: TripMD is proposed as a system to explore driving behavior. TripMD is a motif extraction and summarization system tailored for driving analysis. The system is capable of extracting a rich set of driving patterns. The system provides a space-efficient visualization of the main driving patterns. The system can identify the driving behavior of an unknown driver from other drivers.