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Architecting Artificial Intelligence for Autonomous Cars: The OpenPilot Framework

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Software Architecture (ECSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14212))

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Abstract

Openpilot is a vast open-source semi-automated driving system developed by comma.ai, with 200+ contributors and 750K lines of code according to the OpenHub open-source community-tracking portal. On the one hand, the documentation available gives insights on what Openpilot is capable of doing, how to install it and how people can contribute to it, while the development team posts periodically update on the company’s blog on the state of the project and implementation of new features. On the other hand, this material does little in helping newcomers to embrace the technology with near-zero knowledge over its intended programming model. What is more, not having access to closed-source autonomous-driving framworks does not warrant an appropriate architectural analysis of the framework, the decisions to be made, and the concerns typically emerging in such a decision-making process. This study addresses this gap by exploiting mixed-methods research, featuring (a) an ethnographical study of the community’s operations and (b) a reverse engineering of the OpenPilot codebase both from a structure and operations perspective. This paper is intended as an early-stage adopter study for those software designers, operators, policy-makers and other practitioners aiming at embracing the aforementioned framework and programming model for their own professional endeavours.

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Notes

  1. 1.

    https://policyadvice.net/insurance/insights/self-driving-car-statistics/.

  2. 2.

    https://www.architexa.com/.

  3. 3.

    https://circle.visual-paradigm.com/docs/code-engineering/instant-reverse/how-to-generate-uml-from-cpp/.

  4. 4.

    https://github.com/commaai/Openpilot.git.

  5. 5.

    https://plugins.jetbrains.com/plugin/4509-statistic.

  6. 6.

    a larger and browsable version of this same figure is available online: https://tinyurl.com/ECSA-openpilot.

  7. 7.

    https://www.politesi.polimi.it/bitstream/10589/181889/2/2021_12_Fontana_02.pdf.

  8. 8.

    https://opwiki.readthedocs.io/en/latest/user_manual/gcs_install_op.html.

  9. 9.

    Note that if Thneed is used, a full execution is required only the first time since then the outputs are recorded and can be used to make faster predictions.

  10. 10.

    https://iq.opengenus.org/posenet-model/.

  11. 11.

    The authors would like to thank Mr. Francesco Fontana for his invaluable help in preparing the materials conveyed in this manuscript.

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Authors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Luciano Baresi & Damian A. Tamburri

  2. Eindhoven University of Technology, JADS, Eindhoven, The Netherlands

    Damian A. Tamburri

Authors
  1. Luciano Baresi
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  2. Damian A. Tamburri
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Corresponding author

Correspondence to Damian A. Tamburri .

Editor information

Editors and Affiliations

  1. Wageningen University, Wageningen, The Netherlands

    Bedir Tekinerdogan

  2. Computer Science, Gran Sasso Science Institute, L’Aquila, Italy

    Catia Trubiani

  3. LIRMM, University of Montpellier, Montpellier, France

    Chouki Tibermacine

  4. DIGIP, University of Bergamo, Dalmine, Italy

    Patrizia Scandurra

  5. Superior de Ingeniería Informática, Rey Juan Carlos University, Mostoles, Madrid, Spain

    Carlos E. Cuesta

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Baresi, L., Tamburri, D.A. (2023). Architecting Artificial Intelligence for Autonomous Cars: The OpenPilot Framework. In: Tekinerdogan, B., Trubiani, C., Tibermacine, C., Scandurra, P., Cuesta, C.E. (eds) Software Architecture. ECSA 2023. Lecture Notes in Computer Science, vol 14212. Springer, Cham. https://doi.org/10.1007/978-3-031-42592-9_13

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  • DOI: https://doi.org/10.1007/978-3-031-42592-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42591-2

  • Online ISBN: 978-3-031-42592-9

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