Requirements Engineering for Automotive Perception Systems : An Interview Study
(2023) 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023 In Lecture Notes in Computer Science 13975 LNCS. p.189-205- 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,... (More)
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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- author
- Habibullah, Khan Mohammad ; Heyn, Hans Martin ; Gay, Gregory ; Horkoff, Jennifer ; Knauss, Eric ; Borg, Markus LU ; Knauss, Alessia ; Sivencrona, Håkan and Li, Jing
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Autonomous driving, Driving automation systems, Machine learning, Perception systems, Requirements engineering
- host publication
- Requirements Engineering : Foundation for Software Quality - 29th International Working Conference, REFSQ 2023, Proceedings - Foundation for Software Quality - 29th International Working Conference, REFSQ 2023, Proceedings
- series title
- Lecture Notes in Computer Science
- editor
- Ferrari, Alessio and Penzenstadler, Birgit
- volume
- 13975 LNCS
- pages
- 17 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023
- conference location
- Barcelona, Spain
- conference dates
- 2023-04-17 - 2023-04-20
- external identifiers
-
- scopus:85152590246
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783031297854
- DOI
- 10.1007/978-3-031-29786-1_13
- language
- English
- LU publication?
- yes
- id
- 295a1ad6-de14-4d80-aca1-42bcf97a6b96
- date added to LUP
- 2023-07-19 11:08:56
- date last changed
- 2024-04-19 23:40:30
@inproceedings{295a1ad6-de14-4d80-aca1-42bcf97a6b96,
abstract = {{<p>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.</p>}},
author = {{Habibullah, Khan Mohammad and Heyn, Hans Martin and Gay, Gregory and Horkoff, Jennifer and Knauss, Eric and Borg, Markus and Knauss, Alessia and Sivencrona, Håkan and Li, Jing}},
booktitle = {{Requirements Engineering : Foundation for Software Quality - 29th International Working Conference, REFSQ 2023, Proceedings}},
editor = {{Ferrari, Alessio and Penzenstadler, Birgit}},
isbn = {{9783031297854}},
issn = {{1611-3349}},
keywords = {{Autonomous driving; Driving automation systems; Machine learning; Perception systems; Requirements engineering}},
language = {{eng}},
pages = {{189--205}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Computer Science}},
title = {{Requirements Engineering for Automotive Perception Systems : An Interview Study}},
url = {{http://dx.doi.org/10.1007/978-3-031-29786-1_13}},
doi = {{10.1007/978-3-031-29786-1_13}},
volume = {{13975 LNCS}},
year = {{2023}},
}