GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving
GeoDrive-Bench is a new benchmark for evaluating vision-language models on region-specific traffic rules for autonomous driving. It contains 5,053 human-validated multiple-choice questions from six countries, covering perception, prediction, planning, and region reasoning. The paper also proposes a distillation algorithm to inject local traffic knowledge into VLMs, showing that current VLMs lack robust region-aware reasoning, but the method improves cross-region performance.
[2606.02774] GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving
[Submitted on 1 Jun 2026]
Title:GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.02774 [cs.CV]
(or arXiv:2606.02774v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.02774
arXiv-issued DOI via DataCite (pending registration)
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From: Yingzi Ma [view email] [v1] Mon, 1 Jun 2026 18:36:46 UTC (22,176 KB)
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