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.

翻译：视觉问答（VQA）是自动驾驶中最关键的任务之一，需要精确识别和复杂场景评估。然而，目前尚未建立以问答格式标注的数据集，该格式能够确保从驾驶场景中生成精准的语言表述并进行场景识别。在本工作中，我们提出标记问答（Markup-QA）这一新型数据集标注技术，将问答对嵌入标记结构中。该方法支持同时评估模型在句子生成和视觉问答方面的能力。此外，利用该标注方法，我们设计了NuScenes-MQA数据集。该数据集通过兼顾描述性能力和精准问答，为自动驾驶任务中视觉语言模型的开发提供了有力支持。数据集下载地址为：https://github.com/turingmotors/NuScenes-MQA。