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Setting AI in Context: A Case Study on Defining the Context and Operational Design Domain for Automated Driving

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Requirements Engineering: Foundation for Software Quality (REFSQ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13216))

Abstract

[Context and motivation] For automated driving systems, the operational context needs to be known in order to state guarantees on performance and safety. The operational design domain (ODD) is an abstraction of the operational context, and its definition is an integral part of the system development process. [Question/problem] There are still major uncertainties in how to clearly define and document the operational context in a diverse and distributed development environment such as the automotive industry. This case study investigates the challenges with context definitions for the development of perception functions that use machine learning for automated driving. [Principal ideas/results] Based on qualitative analysis of data from semi-structured interviews, the case study shows that there is a lack of standardisation for context definitions across the industry, ambiguities in the processes that lead to deriving the ODD, missing documentation of assumptions about the operational context, and a lack of involvement of function developers in the context definition. [Contribution] The results outline challenges experienced by an automotive supplier company when defining the operational context for systems using machine learning. Furthermore, the study collected ideas for potential solutions from the perspective of practitioners.

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 957197.

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Notes

  1. 1.

    a Tier 1 supplier develops and sells products and solutions directly to an OEM.

  2. 2.

    Note that due to privacy concern, we intentionally chose not to reveal the respective company.

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Authors and Affiliations

  1. Chalmers University of Technology, 412 96, Gothenburg, Sweden

    Hans-Martin Heyn, Padmini Subbiah, Jennifer Linder & Eric Knauss

  2. University of Gothenburg, 405 30, Gothenburg, Sweden

    Hans-Martin Heyn & Eric Knauss

  3. Veoneer Sweden AB, 103 02, Stockholm, Sweden

    Olof Eriksson

Authors
  1. Hans-Martin Heyn
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  2. Padmini Subbiah
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  3. Jennifer Linder
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  4. Eric Knauss
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  5. Olof Eriksson
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Correspondence to Hans-Martin Heyn .

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Editors and Affiliations

  1. University of Pisa, Pisa, Italy

    Vincenzo Gervasi

  2. University of Cologne, Cologne, Germany

    Andreas Vogelsang

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Heyn, HM., Subbiah, P., Linder, J., Knauss, E., Eriksson, O. (2022). Setting AI in Context: A Case Study on Defining the Context and Operational Design Domain for Automated Driving. In: Gervasi, V., Vogelsang, A. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2022. Lecture Notes in Computer Science, vol 13216. Springer, Cham. https://doi.org/10.1007/978-3-030-98464-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-98464-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98463-2

  • Online ISBN: 978-3-030-98464-9

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