Predriveid: Pre-Trip driver identification from in-vehicle data

Gorkem Kar, Shubham Jain, Marco Gruteser, Jinzhu Chen, Fan Bai, Ramesh Govindan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


.is paper explores the minimal dataset necessary at vehicular edge nodes, to effectively differentiate drivers using data from existing in-vehicle sensors. .is facilitates novel personalization, insurance, advertising, and security applications but can also help in understanding the privacy sensitivity of such data. Existing work on differentiating drivers largely relies on devices that drivers carry, or on the locations that drivers visit to distinguish drivers. Internally, however, the vehicle processes a much richer set of sensor information that is becoming increasingly available to external services. To explore how easily drivers can be distinguished from such data, we consider a system that interfaces to the vehicle bus and executes supervised or unsupervised driver differentiation techniques on this data. To facilitate this analysis and to evaluate the system, we collect in-vehicle data from 24 drivers on a controlled campus test route, as well as 480 trips over three weeks from five shared university mail vans. We also conduct studies between members of a family. The results show that driver differentiation does not require longer sequences of driving telemetry data but can be accomplished with 91% accuracy within 20s the driver enters the vehicle, usually even before the vehicle starts moving.

Original languageAmerican English
Title of host publication2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450350877
StatePublished - Oct 12 2017
Event2nd IEEE/ACM Symposium on Edge Computing, SEC 2017 - San Jose, United States
Duration: Oct 12 2017Oct 14 2017

Publication series

Name2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017


Other2nd IEEE/ACM Symposium on Edge Computing, SEC 2017
Country/TerritoryUnited States
CitySan Jose

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture


  • Driving telemetry data
  • On-board diagnostics
  • Vehicular sensing


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