Design and Automatic Generation of Safety Cases of ML-Enabled Autonomous Driving Systems

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dc.contributor.advisorBelle, Alvine Boaye
dc.contributor.authorSivakumar, Mithila
dc.date.accessioned2024-07-18T21:22:12Z
dc.date.available2024-07-18T21:22:12Z
dc.date.copyright2024-04-03
dc.date.issued2024-07-18
dc.date.updated2024-07-18T21:22:11Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractSafety cases play a pivotal role in ensuring system reliability and acceptability, providing a structured argument supported by evidence. However, gaps in safety case literature hinder comprehensive safety assurance practices. In this thesis, we address this challenge through a three-fold approach. First, we conducted a bibliometric analysis following PRISMA 2020 guidelines to identify trends and knowledge gaps in safety assurance research. The analysis reveals critical areas lacking full safety cases and highlights the need for automated safety case construction. Then, we manually constructed a safety case for an ML-enabled component of an autonomous vehicle. Finally, leveraging large language models like GPT-4, we conducted experiments to automate safety case generation. Results indicate that GPT-4 produces safety cases with moderate accuracy and high semantic similarity to ground truth cases. This comprehensive methodology enhances safety practices, aiding researchers, analysts, and regulators in achieving robust safety assurance in complex systems.
dc.identifier.urihttps://hdl.handle.net/10315/42158
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectAutomotive engineering
dc.subject.keywordsSafety case
dc.subject.keywordsMachine learning
dc.subject.keywordsRE for AI-based safety-critical systems
dc.subject.keywordsSafety assurance
dc.subject.keywordsAutonomous vehicle
dc.subject.keywordsAssurance cases
dc.subject.keywordsBibliometric analysis
dc.subject.keywordsSafety requirements
dc.subject.keywordsUncertainty
dc.subject.keywordsDomain-specific language
dc.subject.keywordsAI and machine learning
dc.subject.keywordsTesting and assurance
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsLarge language models
dc.subject.keywordsGenerative AI
dc.titleDesign and Automatic Generation of Safety Cases of ML-Enabled Autonomous Driving Systems
dc.typeElectronic Thesis or Dissertation

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