Researchers release DriveHierarchy benchmark for VLM driving tests
DriveHierarchy is a new benchmark for evaluating vision-language models (VLMs) in autonomous driving. The framework breaks down driving competence into four ranks: perceptual grounding, contextual memory, mental reasoning, and closed-loop execution. It combines 76,798 question-answer pairs from 84,279 open-source driving frames with 100 simulated closed-loop scenarios on real-world road networks.
Key points
- DriveHierarchy ranks VLM driving skills in four stages: perception, memory, reasoning, and execution
- Uses 76,798 Q&A pairs from 84,279 open-source driving frames and 100 closed-loop scenarios
- Tests 15 VLMs, showing structured capability gaps and open-loop-to-closed-loop connections
The project integrates multiple open-source datasets into a unified platform. Tests on 15 VLMs show the benchmark captures distinct capability variations and links open-loop understanding to closed-loop performance. The goal is to help diagnose and improve VLM-based autonomous driving systems. An anonymized version is available on GitHub.
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