How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study
Vision-Language Models (VLMs) are increasingly proposed for autonomous driving tasks, yet their performance on sequential driving scenes remains poorly characterized, particularly regarding how input configurations affect their capabilities. We introduce VENUSS (VLM Evaluation oN Understanding Sequential Scenes), a framework for systematic sensitivity analysis of VLM performance on sequential driving scenes, establishing baselines for future research. Building upon existing datasets, VENUSS extracts temporal sequences from driving videos, and generates structured evaluations across custom categories. By comparing 25+ existing VLMs across 2,600+ scenarios, we reveal how even top models ach...
| Authors | Roberto Brusnicki, Mattia Piccinini, Johannes Betz |
|---|---|
| Primary category | cs.CV |
| Published | 8 April 2026 |
| Last updated at source | 20 May 2026 |
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