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The DriveMLLM dataset, created by the Institute of Automation, Chinese Academy of Sciences and other institutions, focuses on spatial understanding tasks in autonomous driving scenarios. It contains 880 forward‑camera images covering absolute and relative spatial reasoning tasks, accompanied by rich natural‑language questions. Built upon the nuScenes dataset, the images were strictly selected and annotated to ensure clear visibility of objects and explicit spatial relationships. DriveMLLM aims to evaluate and improve multimodal large language models' spatial reasoning abilities in autonomous driving, addressing complex spatial relation understanding.
