Deep neural networks for driver identification using accelerometer signals from smartphones

Hernández Sánchez, Sara and Fernández Pozo, Rubén and Hernández Gómez, Luis Alfonso (2019). Deep neural networks for driver identification using accelerometer signals from smartphones. In: "International Conference on Business Information Systems 2019", 26/06/2019 - 28/06/2019, Sevilla, España. pp. 1-15. https://doi.org/10.1007%2F978-3-030-20482-2_17.

Description

Title: Deep neural networks for driver identification using accelerometer signals from smartphones
Author/s:
  • Hernández Sánchez, Sara
  • Fernández Pozo, Rubén
  • Hernández Gómez, Luis Alfonso
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Conference on Business Information Systems 2019
Event Dates: 26/06/2019 - 28/06/2019
Event Location: Sevilla, España
Title of Book: Lecture Notes in Business Information Processing
Date: 18 May 2019
Volume: 354
Subjects:
Freetext Keywords: Driving identification; Smartphone; Accelerometers; Deep learning; Neural networks; Fine-tuning
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

With the evolution of the onboard communications services and the applications of ride-sharing, there is a growing need to identify the driver. This identification, within a given driver set, helps in tasks of antitheft, autonomous driving,fleet management systems or automobile insurance. The object of this paper is to identify a driver in the least invasive way possible, using the smartphone that the driver carries inside the vehicle in a free position, and using the minimum number of sensors, only with the tri-axial accelerometer signals from the smartphone. For this purpose, different Deep Neural Networks have been tested, such as the ResNet-50 model and Recurrent Neural Networks. For the training, temporal signals of the accelerometers have been transformed asimages. The accuracies obtained have been 69.92% and 90.31% at top-1 andtop-5 driver level respectively, for a group of 25 drivers. These results outper-form works in the state of the art, which can even utilize more signals (likeGPS- Global Positioning System- measurement data) or extra-equipment (like the Controller Area-Network of the vehicle).

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2015-68172-C2-2-PUnspecifiedUnspecifiedDeep & Subspace Speech Learning

More information

Item ID: 64573
DC Identifier: http://oa.upm.es/64573/
OAI Identifier: oai:oa.upm.es:64573
DOI: 10.1007%2F978-3-030-20482-2_17
Official URL: https://link.springer.com/chapter/10.1007%2F978-3-030-20482-2_17
Deposited by: Memoria Investigacion
Deposited on: 27 Mar 2021 08:26
Last Modified: 27 Mar 2021 08:26
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