OpenDriveLab/DriveLM resource

Health v2 · maintenance only

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Adoption not part of the score

1338 stars · 88 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DriveLM is a research project and dataset for autonomous driving built around Graph Visual Question Answering (GVQA), where QA pairs about perception, prediction, and planning are connected in a graph structure. It includes DriveLM-Data (built on nuScenes and CARLA) and DriveLM-Agent, a VLM-based baseline for end-to-end driving, and served as the basis for the CVPR 2024 Driving-with-Language challenge.

Use cases

• train vision-language models for autonomous driving reasoning
• build end-to-end driving agents with chain-of-thought planning
• evaluate VLMs on perception prediction and planning QA tasks
• research graph-of-thoughts prompting for driving scenarios
• participate in autonomous driving with language challenges
• generate what-if counterfactual reasoning about driving scenes

When to choose

• you need annotated driving QA data with logical dependencies between questions
• you are researching VLM-based end-to-end autonomous driving
• you want a benchmark for structured visual reasoning in driving
• you are entering a driving-with-language challenge

When to avoid

• you need a production-ready autonomous driving stack
• you need real-time vehicle control software
• your project has nothing to do with driving or embodied AI
• you lack GPU resources for training large vision-language models

Facets

dataset · maturity active

machine-learning nlp computer-vision prompt-engineering rag autonomous-vehicles large-language-models artificial-intelligence computer-vision python vision-language-models graph-visual-question-answering end-to-end-driving chain-of-thought nuscenes carla benchmark-dataset eccv-2024 natural-language-processing linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenDriveLab/DriveLM")