Requirements Representations in Machine Learning-Based Automotive Perception Systems Development for Multi-party Collaboration
Paper i proceeding, 2025

[Context and motivation] Advancements in machine learning (ML) have impacted driving automation systems (DAS) as well as ML-based perception systems. This increased complexity leads to intensified multi-party collaboration and special needs for requirements representations. [Question(s)/Aim(s)] Well-defined requirements are essential in this safety-oriented domain, but requirements engineering (RE) for ML-enabled perception systems remains challenging. We aim to enrich the cross-section of requirements representations, multi-party collaboration, and the need for a shared language for ML-enabled automotive perception systems development in DAS. [Method] An Interview study with ten experts from a major automotive original equipment manufacturer (OEM), its suppliers, and researchers is conducted, followed by a thematic analysis. [Principal idea(s)/Results] Current practices for requirements representations in this context rely on natural language, operational design domains (ODDs), and key performance indicators (KPIs). Multi-party collaboration uses formal communication, while RE for ML-enabled systems faces challenges from a lack of mature standards and causality issues. [Contribution/Conclusion] We identify previously unrecognized practical limitations in requirements representation for ML-based perception systems and multi-party collaboration. We highlight effective practices, and our findings suggest that a shared language and reference architecture could address key RE challenges.

Autonomous driving

Automotive

Requirements representation

Machine Learning

Perception systems

Requirements engineering

Driving automation system

Shared language

Författare

Hina Saeeda

Göteborgs universitet

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Zuzana Rohacova

Student vid Chalmers

Oskar Jakobsson

Student vid Chalmers

Hans-Martin Heyn

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Göteborgs universitet

Eric Knauss

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Göteborgs universitet

Alessia Knauss

Zenseact AB

Jennifer Horkoff

Göteborgs universitet

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

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

03029743 (ISSN) 16113349 (eISSN)

Vol. 15588 LNCS 197-213
9783031885303 (ISBN)

31st International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2025
Barcelona, Spain,

FAMER - Flerparts kravhantering i samarbete för produktutveckling

FFI - Fordonsstrategisk forskning och innovation (2023-00771), 2023-09-01 -- 2026-08-31.

Ämneskategorier (SSIF 2025)

Programvaruteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1007/978-3-031-88531-0_14

Mer information

Senast uppdaterat

2025-04-28