Requirements Representations in Machine Learning-based Automotive Perception Systems Development for Multi-Party CollaborationEvaluation Paper
This program is tentative and subject to change.
[Context and motivaton] 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 contribute to 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] A case 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.
This program is tentative and subject to change.
Tue 8 AprDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
16:00 - 17:10 | |||
16:00 35mTalk | LACE-HC: A Lightweight Attention-Based Classifier for Efficient Hierarchical Classification of Software RequirementsTechnical Paper Research Track Krupa Patel Bhagwan Mahavir University, Tanvi Trivedi Bhagwan Mahavir University, Unnati Shah Utica University, USA | ||
16:35 35mTalk | Requirements Representations in Machine Learning-based Automotive Perception Systems Development for Multi-Party CollaborationEvaluation Paper Research Track Hina Saeeda Chalmers University Sweden, Zuzana Rohacova Chalmers University of Technology, Oskar Jakobsson Chalmers University of Technology, Hans-Martin Heyn University of Gothenburg & Chalmers University of Technology, Eric Knauss Chalmers | University of Gothenburg, Alessia Knauss Zenseact AB, Jennifer Horkoff Chalmers and the University of Gothenburg |