Improving Driver Identification for the Next-Generation of In-Vehicle Software Systems

This paper deals with driver identification and fingerprinting and its application for enhanced driver profiling and car security in connected cars. The authors introduce a new driver identification model based on collected data from smartphone sensors, and/or the OBD-II protocol, using convolutional neural networks, and recurrent neural networks (long short-term memory) RNN/LSTM. Unlike the existing works, the authors use a cross-validation technique that provides reproducible results when applied on unseen realistic data. The authors also studied the robustness of the model to sensor data anomalies. The obtained results show that the authors' model accuracy remains acceptable even when the rate of the anomalies increases substantially. Finally, the proposed model was tested on different datasets and implemented in Automotive Grade Linux Framework, as a real-time anti-theft and the driver profiling system.

Language

  • English

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Filing Info

  • Accession Number: 01716535
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 26 2019 9:54AM