Abstract
Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. Challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.
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Notes
- 1.
In this paper, we focus specifically on ML-based perception systems for DAS, but often use the term perception systems as shorthand.
- 2.
A recent submission has used the same study data, but focuses on the annotation, data, and ecosystems and business themes [9].
- 3.
The interview guide can be found at: https://doi.org/10.7910/DVN/HCMVL1.
References
Acuna, D., Philion, J., Fidler, S.: Towards optimal strategies for training self-driving perception models in simulation. Adv. Neural. Inf. Process. Syst. 34, 1686–1699 (2021)
Ågren, S.M., Knauss, E., Heldal, R., Pelliccione, P., Malmqvist, G., Bodén, J.: The impact of requirements on systems development speed: a multiple-case study in automotive. Requirements Eng. 24(3), 315–340 (2019)
Ahmad, K., Bano, M., Abdelrazek, M., Arora, C., Grundy, J.: What’s up with requirements engineering for artificial intelligence systems? In: 2021 IEEE 29th International Requirements Engineering Conference(RE), pp. 1–12. IEEE (2021)
Ali, M.A., Yap, N.K., Ghani, A.A.A., Zulzalil, H., Admodisastro, N.I., Najafabadi, A.A.: A systematic mapping of quality models for AI systems, software and components. Appl. Sci. 12(17), 8700 (2022)
Allmann, C., Winkler, L., Kölzow, T., et al.: The requirements engineering gap in the oem-supplier relationship. J. Univer. Knowl. Manage. 1(2), 103–111 (2006)
Belani, H., Vukovic, M., Car, Ž.: Requirements engineering challenges in building AI-based complex systems. In: 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pp. 252–255. IEEE (2019)
Borg, M., et al.: Safely entering the deep: A review of verification and validation for machine learning and a challenge elicitation in the automotive industry. arXiv preprint arXiv:1812.05389 (2018)
Habibullah, K.M., Gay, G., Horkoff, J.: Non-functional requirements for machine learning: An exploration of system scope and interest. In: 2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI), pp. 29–36. IEEE (2022)
Heyn, H.M., et al.: Automotive perception software development: Data, annotation, and ecosystem challenges, (Submitted)
Heyn, H.M., et al.: Requirement engineering challenges for AI-intense systems development. In: 2021 IEEE/ACM 1st Workshop on AI Engineering-SE for AI (WAIN), pp. 89–96. IEEE (2021)
Heyn, H.-M., Subbiah, P., Linder, J., Knauss, E., Eriksson, O.: Setting AI in context: a case study on defining the context and operational design domain for automated driving. In: Gervasi, V., Vogelsang, A. (eds.) REFSQ 2022. LNCS, vol. 13216, pp. 199–215. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98464-9_16
Jayatilleke, S., Lai, R.: A systematic review of requirements change management. Inf. Softw. Technol. 93, 163–185 (2018)
Liebel, G., Tichy, M., Knauss, E., Ljungkrantz, O., Stieglbauer, G.: Organisation and communication problems in automotive requirements engineering. Requirements Eng. 23(1), 145–167 (2018)
M. Mahally, M., Staron, M., Bosch, J.: Barriers and enablers for shortening software development lead-time in mechatronics organizations: A case study. In: Proceedings of the 2015 10th Joint Meeting on Foundations of SE, pp. 1006–1009 (2015)
Mallozzi, P., Pelliccione, P., Knauss, A., Berger, C., Mohammadiha, N.: Autonomous vehicles: state of the art, future trends, and challenges. In: Automotive Systems and SE, pp. 347–367 (2019)
Pernstål, J., Gorschek, T., Feldt, R., Florén, D.: Software process improvement in inter-departmental development of software-intensive automotive systems – a case study. In: Heidrich, J., Oivo, M., Jedlitschka, A., Baldassarre, M.T. (eds.) PROFES 2013. LNCS, vol. 7983, pp. 93–107. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39259-7_10
Ribeiro, Q.A., Ribeiro, M., Castro, J.: Requirements engineering for autonomous vehicles: a systematic literature review. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 1299–1308 (2022)
Saldaña, J.: The coding manual for qualitative researchers. The coding manual for qualitative researchers, pp. 1–440 (2021)
Staron, M.: Requirements engineering for automotive embedded systems. In: Automotive Systems and Software Engineering, pp. 11–28. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12157-0_2
Sutcliffe, A.: Scenario-based requirements engineering. In: Proceedings of 11th IEEE International Requirements Engineering Conference 2003, pp. 320–320. IEEE Computer Society (2003)
Villamizar, H., Escovedo, T., Kalinowski, M.: Requirements engineering for machine learning: A systematic mapping study. In: 2021 47th Euromicro Conference on SE and Advanced Applications (SEAA), pp. 29–36. IEEE (2021)
Vogelsang, A., Borg, M.: Requirements engineering for machine learning: Perspectives from data scientists. In: 2019 IEEE 27th International Requirements Engineering Conference on Workshops (REW), pp. 245–251. IEEE (2019)
Wohlrab, R., Steghöfer, J.P., Knauss, E., Maro, S., Anjorin, A.: Collaborative traceability management: Challenges and opportunities. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 216–225. IEEE (2016)
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Support for this project was provided by Vinnova pre-study 2021-02572. We thank all participants.
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Habibullah, K.M. et al. (2023). Requirements Engineering for Automotive Perception Systems: An Interview Study. In: Ferrari, A., Penzenstadler, B. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2023. Lecture Notes in Computer Science, vol 13975. Springer, Cham. https://doi.org/10.1007/978-3-031-29786-1_13
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