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
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorLev Sorokin →
Search-based Testing of Vision Language Models for In-Car Scene Understanding
- Linked via arxiv authorChen Yang →
Search-based Testing of Vision Language Models for In-Car Scene Understanding
- Linked via arxiv authorKen E. Friedl →
Search-based Testing of Vision Language Models for In-Car Scene Understanding
- Linked via arxiv authorAndrea Stocco →
Search-based Testing of Vision Language Models for In-Car Scene Understanding