The paper proposes MLLM-SUL, an image-based multimodal language model that jointly generates driving-scene captions and localizes risk objects, reporting state-of-the-art scores on DRAMA-ROLISP and an extended DRAMA-SRIS dataset.
Drive like a human: Rethinking autonomous driving with large language models,
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MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios
The paper proposes MLLM-SUL, an image-based multimodal language model that jointly generates driving-scene captions and localizes risk objects, reporting state-of-the-art scores on DRAMA-ROLISP and an extended DRAMA-SRIS dataset.