CLARU
eddyhkchiu2024apache-2.0

V2V-GoT-QA

A multimodal LLM-based dataset for cooperative autonomous driving with graph-of-thoughts reasoning, containing 110K training and 31K testing QA pairs across perception, prediction, and planning tasks.

Downloads109
Episodes141000
Likes2

Why This Matters for Physical AI

This dataset advances cooperative autonomous driving research by providing multimodal reasoning capabilities for vehicle-to-vehicle coordination, enabling LLMs to perform occlusion-aware perception, planning-aware prediction, and collision avoidance.

Technical Profile

Modalities
rgblanguage
Robot Embodiments
autonomous_vehicle
Action Space
waypoints
Environment
outdoor
Task Types
navigationplanningperceptionprediction
Episodes
141000
Annotation Types
language_instructionsaction_labelsbounding_boxes
License
apache-2.0
Part of the V2V-GoT family

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