AI RESEARCH
Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset
arXiv CS.CV
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ArXi:2606.02273v1 Announce Type: new Understanding subtle driver actions is essential for building reliable driver monitoring systems. Existing visionlanguage models (VLMs) are trained on general datasets and struggle to recognize fine distinctions in driver behaviors. This paper addresses this limitation by creating a detailed natural language version of the Drive&Act dataset. We evaluate three VLMs on our new benchmark using LLM-based scoring methods. Their performance on the new benchmark shows that they cannot reliably generate accurate fine-grained driver activity descriptions.