Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

• 🌟 Welcome to check out our new work DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models and awesome knowledge-driven AD!
• 🔥 Try out our web demo on Hugging face🤗 without any deployment!

Pre-requirement

pip install highway-env
pip install -r requirements.txt

Running HELLM.py allows you to experience LLM’s closed-loop driving in HighwayEnv. First, you need to modify config.yaml to configure your LLM.

OPENAI_API_TYPE: 'azure' #'azure'  OR 'openai'
# for 'openai'
OPENAI_KEY: 'sk-xxxxxxxxxxx' # your openai key
# for 'azure'
AZURE_MODEL: 'XXXXX' # your deploment_model_name 
AZURE_API_BASE: https://xxxxxxxx.openai.azure.com/ # your deployment endpoint
AZURE_API_KEY: 'xxxxxx' # your deployment key
AZURE_API_VERSION: '2023-03-15-preview'

We use GPT-3.5 (recommended the models with 8k+ max token) as the default LLM, but you can also refer to LangChain-Large Language Models to define your own LLM. In this case, you need to modify lines 24-40 of HELLM.py to configure your own LLM.

if OPENAI_CONFIG['OPENAI_API_TYPE'] == 'azure':
    os.environ["OPENAI_API_TYPE"] = OPENAI_CONFIG['OPENAI_API_TYPE']
    os.environ["OPENAI_API_VERSION"] = OPENAI_CONFIG['AZURE_API_VERSION']
    os.environ["OPENAI_API_BASE"] = OPENAI_CONFIG['AZURE_API_BASE']
    os.environ["OPENAI_API_KEY"] = OPENAI_CONFIG['AZURE_API_KEY']
    llm = AzureChatOpenAI(
        deployment_name=OPENAI_CONFIG['AZURE_MODEL'],
        temperature=0,
        max_tokens=1024,
        request_timeout=60
    )
elif OPENAI_CONFIG['OPENAI_API_TYPE'] == 'openai':
    os.environ["OPENAI_API_KEY"] = OPENAI_CONFIG['OPENAI_KEY']
    llm = ChatOpenAI(
        temperature=0,
        model_name='gpt-3.5-turbo-16k-0613', # or any other model with 8k+ context
        max_tokens=1024,
        request_timeout=60
    )

Then, by running python HELLM.py, you can see the process of LLM making decisions using tools.

Try it with your own image in Hugging face🤗 or deploy your own with this notebook!

@article{fu2023drive,
  title={Drive like a human: Rethinking autonomous driving with large language models},
  author={Fu, Daocheng and Li, Xin and Wen, Licheng and Dou, Min and Cai, Pinlong and Shi, Botian and Qiao, Yu},
  journal={arXiv preprint arXiv:2307.07162},
  year={2023}
}

We would like to thank the authors and developers of the following projects, this project is built upon these great open-sourced projects.

• highway-env
• LangChain
• LLaMA-Adapter

• If you have any questions, please report issues on GitHub.