TripMD: Driving patterns investigation via motif analysis

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