Deep Neural Networks for Driver Identification Using Accelerometer Signals from Smartphones

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)


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 as images. The accuracies obtained have been 69.92% and 90.31% at top-1 and top-5 driver level respectively, for a group of 25 drivers. These results outperform works in the state of the art, which can even utilize more signals (like GPS- Global Positioning System- measurement data) or extra-equipment (like the Controller Area-Network of the vehicle).


Driving identification Smartphone Accelerometers Deep learning Neural networks Fine-tuning 



We thank Drivies (PhoneDrive S.L.) for the support in the driving research and the access to the journeys database. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and the European Union (FEDER) within the framework of the project DSSL: “Deep & Subspace Speech Learning (TEC2015-68172-C2-2-P)”.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Signals, Systems and RadiocommunicationsUniversidad Politécnica de MadridMadridSpain
  2. 2.Group of Biometry, Biosignals, Security, and Smart MobilityUniversidad Politécnica de MadridMadridSpain

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