Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset
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. Based on the labeled Drive&Act dataset we create a new Drive&Act description dataset containing finegrained descriptions to train VLMs on driver...
| Authors | David J. Lerch, Sarath Mulugurthi, Manuel Martin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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