Requirements Engineering for Automotive Perception Systems: An Interview Study
Paper i proceeding, 2023
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.
Driving automation systems
Machine learning
Requirements engineering
Autonomous driving
Perception systems
Författare
Khan Mohammad Habibullah
Software Engineering 1
Göteborgs universitet
Hans-Martin Heyn
Göteborgs universitet
Software Engineering 1
Gregory Gay
Software Engineering 1
Göteborgs universitet
Jennifer Horkoff
Göteborgs universitet
Software Engineering 1
Eric Knauss
Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering
Göteborgs universitet
M. Borg
Lunds universitet
Alessia Knauss
Zenseact AB
Håkan Sivencrona
Zenseact AB
Jing Li
Kognic
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
03029743 (ISSN) 16113349 (eISSN)
Vol. 13975 LNCS 189-2059783031297854 (ISBN)
29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023
Barcelona, Spain,
Barcelona, Spain,
Ämneskategorier (SSIF 2011)
Produktionsteknik, arbetsvetenskap och ergonomi
Annan data- och informationsvetenskap
Datorsystem
DOI
10.1007/978-3-031-29786-1_13