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A Neural-Network-Based Framework for Supporting Driver Classification and Analysis

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12951))

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Abstract

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. The infrastructure in which these information can be used to improve safety and security are provided by Ultra-response connectivity networks with a latency of below 10 ms. In this paper, we propose a service-oriented architecture based on a fully connected neural network architecture considering position-based features aimed to detect in real-time: (i) the driver and (ii) the driving style, with the goal of providing an architecture for increasing security and safety in automotive context. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results.

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Notes

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    https://www.tensorflow.org/.

  2. 2.

    https://keras.io/.

  3. 3.

    https://matplotlib.org/.

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Author information

Authors and Affiliations

  1. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  2. IIT-CNR, Pisa, Italy

    Francesco Mercaldo & Fabio Martinelli

Authors
  1. Alfredo Cuzzocrea
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  2. Francesco Mercaldo
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  3. Fabio Martinelli
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Cuzzocrea, A., Mercaldo, F., Martinelli, F. (2021). A Neural-Network-Based Framework for Supporting Driver Classification and Analysis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-86970-0_3

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  • Print ISBN: 978-3-030-86969-4

  • Online ISBN: 978-3-030-86970-0

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