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

Perception systems

Requirements engineering

Autonomous driving


Khan Mohammad Habibullah

Göteborgs universitet

Hans-Martin Heyn

Göteborgs universitet

Gregory Gay

Göteborgs universitet

Jennifer Horkoff

Göteborgs universitet

Eric Knauss

Göteborgs universitet

M. Borg

Lunds universitet

Alessia Knauss

Zenseact AB

Håkan Sivencrona

Zenseact AB

Jing Li


Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13975 LNCS 189-205
9783031297854 (ISBN)

29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023
Barcelona, Spain,


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