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+ ---
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+ license: apache-2.0
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+ ---
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+ # LMDrive Model Card
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+ ## Model details
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+ **Model type:**
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+ LMDrive is an end-to-end, closed-loop, language-based autonomous driving framework, which interacts with the dynamic environment via multi-modal multi-view sensor data and natural language instructions.
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+ The model weight is for the vision encoder used in the LMDrive.
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+ **Model date:**
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+ LMDrive-1.0 was trained in November 2023.
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+ **Paper or resources for more information:**
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+ Github: https://github.com/opendilab/LMDrive/README.md
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+ Paper: https://arxiv.org/abs/2312.07488
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+ **Where to send questions or comments about the model:**
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+ https://github.com/opendilab/LMDrive/issues
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+ ## Intended use
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+ **Primary intended uses:**
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+ The primary use of LMDrive is research on large multimodal models for autonomous driving.
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+ **Primary intended users:**
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+ The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence.
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+ ## Training dataset
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+ - 64K instruction-sensor-control data clips collected in the CARLA simulator. [dataset_webpage](https://huggingface.co/datasets/deepcs233/LMDrive)
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+ - where each clip includes one navigation instruction, several notice instructions, a sequence of multi-modal multi-view sensor data, and control signals. The duration of the clip spans from 2 to 20 seconds
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+ ## Evaluation benchmark
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+ LangAuto, LangAuto-short, LangAuto-tiny, LangAuto-notice