Requirement representation for safety critical and fairness aware automotive perception systems - Identifying requirements representation challenges for multi party collaboration.

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dc.contributor.authorJakobsson, Oskar
dc.contributor.authorRohacova, Zuzana
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineeringen
dc.contributor.examinerPenzenstadler, Birgit
dc.contributor.supervisorHeyn, Hans-Martin
dc.contributor.supervisorSaeeda, Hina
dc.date.accessioned2024-09-10T15:05:42Z
dc.date.available2024-09-10T15:05:42Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractBackground: Advancements in the field of machine learning (ML), have unlocked new capabilities for Driving Automation Systems (DAS). These DAS systems rely on the input from automotive perception systems. These systems exist in a safety critical domain, and well-defined requirements are key to ensure that they can operate safely. However, requirements engineering (RE) for ML-enabled systems has been identified as a challenge in research. Aim: The thesis aimed to investigate current approaches in RE for automotive perception systems, and identify what challenges exist and which processes work well. Specifically, approaches for requirement representations, model kinds, templates, and structures. The thesis also wanted to explore if a shared language, in regards to domain description, reference system architecture, and reference information model, could help mitigate potential challenges without hindering approaches that work well. Methods: An exploratory case study was conducted by interviewing experts in the automotive field. This included participants from a major automotive OEM, suppliers to said OEM and experienced researchers in the automotive field. In total ten interviewees were consulted. Results: The challenges and what works well in the current processes in the case study companies were identified through thematic analysis of the interview data. The thesis explored the potential of a shared language to mitigate these challenges by discussing the topic with interviewees and observing brainstorming workshops for the creation of the shared language. Conclusions: The thesis shows that there is a lack of industry standards in RE for ML-enabled automotive perception systems, which complicates multi-party development. According to interviewees, the shared language has potential to alleviate the identified challenges. However, the feasibility of the shared language is still unclear.
dc.identifier.coursecodeDATX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308565
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectRequirements engineering
dc.subjectRequirements
dc.subjectPerception systems
dc.subjectThesis
dc.subjectRequirements representation
dc.subjectSafety-critical
dc.subjectMulti-party development
dc.subjectAutomotive
dc.subjectShared language
dc.titleRequirement representation for safety critical and fairness aware automotive perception systems - Identifying requirements representation challenges for multi party collaboration.
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSoftware engineering and technology (MPSOF), MSc
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