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license: apache-2.0
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# LMDrive Model Card

## Model details

**Model type:**
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.

**Model date:**
LMDrive-1.0 (based on Vicuna-v1.5-7B) was trained in November 2023. The original Vicuna-v1.5 also needs to be downloaded.

**Paper or resources for more information:**

Github: https://github.com/opendilab/LMDrive/README.md

Paper: https://arxiv.org/abs/2312.07488

**Related weights for the vision encoder**

https://huggingface.co./deepcs233/LMDrive-vision-encoder-r50-v1.0

**Where to send questions or comments about the model:**

https://github.com/opendilab/LMDrive/issues



## Intended use
**Primary intended uses:**

The primary use of LMDrive is research on large multimodal models for autonomous driving.

**Primary intended users:**

The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence.

## Training dataset

- 64K instruction-sensor-control data clips collected in the CARLA simulator. [dataset_webpage](https://huggingface.co./datasets/deepcs233/LMDrive)
  - 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 

## Evaluation benchmark
LangAuto, LangAuto-short, LangAuto-tiny, LangAuto-notice