Search-based Testing of Vision Language Models for In-Car Scene Understanding
In the automotive domain, in-car scene understanding (ISU) enables the detection of safety-critical events, such as driver distraction, and supports drivers or passengers by analyzing the in-car scene and adapting the environment (e.g., ambient lighting). The industry is increasingly exploring vision-language models (VLMs) to interpret camera-recorded in-car scenes and extract information for downstream reasoning tasks. However, VLMs may generate incomplete, erroneous, or misleading scene descriptions, highlighting the need for systematic testing. Collecting real in-vehicle data is costly, dif
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%vlm-starter →
- PossiblePossibly related (embedding) · 47%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Search-based Testing of Vision Language Models for In-Car Sc” ≈ “VioletVision-3B””
- LinkedLinked via arxiv author · 85%Lev Sorokin →
“Search-based Testing of Vision Language Models for In-Car Scene Understanding”
- LinkedLinked via arxiv author · 85%Chen Yang →
“Search-based Testing of Vision Language Models for In-Car Scene Understanding”
- LinkedLinked via arxiv author · 85%Ken E. Friedl →
“Search-based Testing of Vision Language Models for In-Car Scene Understanding”
- LinkedLinked via arxiv author · 85%Andrea Stocco →
“Search-based Testing of Vision Language Models for In-Car Scene Understanding”
- PossiblePossibly related (embedding) · 46%Blaizzy/mlx-vlm →