GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving

Researchers introduced GeoDrive-Bench, a benchmark of 5,053 human-validated multiple-choice questions across six countries to test vision-language models on region-specific traffic rules and driving behavior. Testing nine state-of-the-art VLMs revealed significant performance variations across different driving cultures, indicating current models lack robust region-aware intelligence. The benchmark serves as both a diagnostic tool and training resource for developing autonomous driving systems that can adapt to local traffic conventions worldwide.

arXiv:2606.02774v1 Announce Type: new Abstract: Vision-language models VLMs for autonomous driving have shown promising performance, but their ability to handle region-specific traffic rules remains underexplored, raising uncertainties about their deployment across diverse global settings. We therefore introduce GeoDrive-Bench, a novel benchmark that enables the systematic investigation of VLMs' geo-culturally grounded driving reasoning. We curated 5,053 human-validated multiple-choice QA pairs across six countries covering diverse driving cultures. Specifically, we emphasize four driving tasks: perception, prediction, planning, and region reasoning. Each question requires models to infer the correct driving behavior from visual evidence and local traffic conventions without explicit country labels. Beyond evaluation, we further design a distillation algorithm that injects region-specific traffic-rule knowledge into the internal representations of VLMs, enabling models to better align visual scene understanding with local driving policies. Experiments on nine state-of-the-art VLMs show substantial performance variations across geo-driving cultures for each task, while our proposed baseline models exhibit improved geo-cultural reasoning across regions. These results suggest that current VLMs still lack robust region-aware driving intelligence and highlight GeoDrive-Bench as a diagnostic and training-oriented testbed for deployable autonomous driving foundation models.