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How Does Simulation-Based Testing for Self-Driving Cars Match Human Perception?

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dc.contributor.authorBirchler, Christian
dc.contributor.authorMohammed, Tanzil Kombarabettu
dc.contributor.authorRani, Pooja
dc.contributor.authorNechita, Teodora
dc.contributor.authorKehrer, Timo
dc.contributor.authorPanichella, Sebastiano
dc.contributor.editorKoziolek, Anne
dc.contributor.editorLamprecht, Anna-Lena
dc.contributor.editorThüm, Thomas
dc.contributor.editorBurger, Erik
dc.date.accessioned2025-02-14T09:36:31Z
dc.date.available2025-02-14T09:36:31Z
dc.date.issued2025
dc.description.abstractSoftware metrics such as coverage or mutation scores have been investigated for the automated quality assessment of test suites. While traditional tools rely on software metrics, the field of self-driving cars (SDCs) has primarily focused on simulation-based test case generation using quality metrics such as the out-of-bound (OOB) parameter to determine if a test case fails or passes. However, it remains unclear to what extent this quality metric aligns with the human perception of the safety and realism of SDCs. To address this (reality) gap, we conducted an empirical study involving 50 participants to investigate the factors that determine how humans perceive SDC test cases as safe, unsafe, realistic, or unrealistic. To this aim, we developed a framework leveraging virtual reality (VR) technologies, called SDC-Alabaster, to immerse the study participants into the virtual environment of SDC simulators. Our findings indicate that the human assessment of safety and realism of failing/passing test cases can vary based on different factors, such as the test’s complexity and the possibility of interacting with the SDC. Especially for the assessment of realism, the participants’ age leads to a different perception. This study highlights the need for more research on simulation testing quality metrics and the importance of human perception in evaluating SDCs.en
dc.identifier.doi10.18420/se2025-41
dc.identifier.eissn2944-7682
dc.identifier.issn2944-7682
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45804
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofSoftware Engineering 2025
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-360
dc.subjectSoftware Testing
dc.subjectSelf-Driving Cars
dc.subjectSimulation
dc.subjectVR
dc.subjectHuman Perception
dc.titleHow Does Simulation-Based Testing for Self-Driving Cars Match Human Perception?en
mci.conference.date22.-28. Februar 2025
mci.conference.locationKarlsruhe
mci.conference.sessiontitleScientific Programme
mci.reference.pages123-124

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