Robusto-2: Benchmarking Humans & VLMs for Autonomous Driving in Lima & New York City
As Self-Driving Cars continue to expand internationally and use multi-modal systems such as VLMs as a cognitive backbone for their Action models; how well will these systems generalize in new settings, in particular out-of-distribution (OOD) edge-case scenarios in new geographies? In this paper, we study this open question by providing a full factorial analysis with human drivers of Lima, human drivers from New York City, and VLMs and showing them dashcam footage collected from Lima and New York City -- prompting them with a variety of questions under a Visual Question Answering (VQA) paradigm. In particular, we pick these two cities as they are highly challenging driving locations where ...
| Authors | Adrian Cespedes, Marcelo Chincha, Dunant Cusipuma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 12 September 2026 |
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