### What is GVQA?
The most exciting aspect of the dataset is that the questions and answers (`QA pairs`) are connected in a graph-style structure, with QA pairs as every node and potential logical progression as the edges. The reason for doing this in the AD domain is that AD tasks are well-defined per stage, from raw sensor input to final control action through perception, prediction and planning.

Its key difference to prior VQA tasks for AD is the availability of logical dependencies between QAs, which can be used to guide the answering process. Below is a demo video illustrating the idea.

https://github.com/OpenDriveLab/DriveLM/assets/54334254/988472a8-d7b9-4685-b4b8-7a0e77f68265
