  Visual Question Answering (VQA) is one of the most important tasks in
autonomous driving, which requires accurate recognition and complex situation
evaluations. However, datasets annotated in a QA format, which guarantees
precise language generation and scene recognition from driving scenes, have not
been established yet. In this work, we introduce Markup-QA, a novel dataset
annotation technique in which QAs are enclosed within markups. This approach
facilitates the simultaneous evaluation of a model's capabilities in sentence
generation and VQA. Moreover, using this annotation methodology, we designed
the NuScenes-MQA dataset. This dataset empowers the development of vision
language models, especially for autonomous driving tasks, by focusing on both
descriptive capabilities and precise QA. The dataset is available at
https://github.com/turingmotors/NuScenes-MQA.
