research-article

PredriveID: pre-trip driver identification from in-vehicle data

Authors:

Marco Gruteser,

Ramesh GovindanAuthors Info & Claims

SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing

Article No.: 2, Pages 1 - 12

https://doi.org/10.1145/3132211.3134462

Published: 12 October 2017 Publication History

Abstract

This paper explores the minimal dataset necessary at vehicular edge nodes, to effectively differentiate drivers using data from existing in-vehicle sensors. This 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 after the driver enters the vehicle, usually even before the vehicle starts moving.

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Cited By

Li DHao RWei ZLiu J(2024)A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge ComputingMathematics10.3390/math1203049612:3(496)Online publication date: 5-Feb-2024
https://doi.org/10.3390/math12030496
Pesé MSchauer JMohan MJoseph CShin KMoore J(2023)PRICAR: Privacy Framework for Vehicular Data Sharing with Third Parties2023 IEEE Secure Development Conference (SecDev)10.1109/SecDev56634.2023.00032(184-195)Online publication date: 18-Oct-2023
https://doi.org/10.1109/SecDev56634.2023.00032
Gozubuyuk BBailey WEverson DDong ZCheng LPesé M(2023)An Overview of Security in Connected and Autonomous Vehicles2023 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)10.1109/AIoTSys58602.2023.00052(206-213)Online publication date: 19-Oct-2023
https://doi.org/10.1109/AIoTSys58602.2023.00052
Show More Cited By

Index Terms

PredriveID: pre-trip driver identification from in-vehicle data
1. Computer systems organization
  1. Real-time systems
    1. Real-time system architecture
2. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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Published In

cover image ACM Conferences

SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing

October 2017

365 pages

ISBN:9781450350877

DOI:10.1145/3132211

General Chair:
Junshan Zhang
Arizona State University
,
Program Chairs:
Mung Chiang
Purdue University and Princeton Edge Lab
,
Bruce Maggs
Akamai/Duke University

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2017

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SEC '17

Sponsor:

SIGMOBILE
IEEE-CS\DATC

SEC '17: IEEE/ACM Symposium on Edge Computing

October 12 - 14, 2017

California, San Jose

Acceptance Rates

SEC '17 Paper Acceptance Rate 20 of 41 submissions, 49%;

Overall Acceptance Rate 40 of 100 submissions, 40%

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Cited By

Li DHao RWei ZLiu J(2024)A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge ComputingMathematics10.3390/math1203049612:3(496)Online publication date: 5-Feb-2024
https://doi.org/10.3390/math12030496
Pesé MSchauer JMohan MJoseph CShin KMoore J(2023)PRICAR: Privacy Framework for Vehicular Data Sharing with Third Parties2023 IEEE Secure Development Conference (SecDev)10.1109/SecDev56634.2023.00032(184-195)Online publication date: 18-Oct-2023
https://doi.org/10.1109/SecDev56634.2023.00032
Gozubuyuk BBailey WEverson DDong ZCheng LPesé M(2023)An Overview of Security in Connected and Autonomous Vehicles2023 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)10.1109/AIoTSys58602.2023.00052(206-213)Online publication date: 19-Oct-2023
https://doi.org/10.1109/AIoTSys58602.2023.00052
Tseng PLin PKristianto E(2023)Vehicle theft detection by generative adversarial networks on driving behaviorEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105571117:PBOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105571
Nasr Azadani MBoukerche A(2022)Driving Behavior Analysis Guidelines for Intelligent Transportation SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.307614023:7(6027-6045)Online publication date: Jul-2022
https://doi.org/10.1109/TITS.2021.3076140
Lim JBaek YChae B(2022)Temporal Early Exiting With Confidence Calibration for Driver Identification Based on Driving Sensing DataIEEE Access10.1109/ACCESS.2022.322857310(132095-132107)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3228573
Hochstetler J(2021)An Extensible Computing Architecture Design for Connected Autonomous Vehicle Systemundefined10.12794/metadc1808452Online publication date: May-2021
https://doi.org/10.12794/metadc1808452
Kwak BHan MKim H(2021)Driver Identification Based on Wavelet Transform Using Driving PatternsIEEE Transactions on Industrial Informatics10.1109/TII.2020.299991117:4(2400-2410)Online publication date: Apr-2021
https://doi.org/10.1109/TII.2020.2999911
Shi WLiu LShi WLiu L(2021)Dataset and BenchmarkComputing Systems for Autonomous Driving10.1007/978-3-030-81564-6_5(109-142)Online publication date: 30-Jul-2021
https://doi.org/10.1007/978-3-030-81564-6_5
Shi WLiu LShi WLiu L(2021)Computing Framework for Autonomous DrivingComputing Systems for Autonomous Driving10.1007/978-3-030-81564-6_2(19-55)Online publication date: 30-Jul-2021
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