Awesome
Drive Like A Human
<!-- [![Static Badge](https://img.shields.io/badge/Homepage-Drive_Like_A_Human-00cec9)]() -->Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
NEWS
- 🔥 Check out our work on ICLR 24 DiLu: A Knowledge-Driven Approach to Autonomous Driving with LLM !
- 🌟 We present a survey on Awesome Knowledge-driven AD.
Closed-loop interaction ability in driving scenarios
https://github.com/PJLab-ADG/DriveLikeAHuman/assets/18390668/0ec8e901-9dc1-4c89-81d6-994309a49630
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.
Reasoning ability with common sense
Try it with your own image in Hugging face🤗 or deploy your own with this notebook!
Performance enhancement through memorization ability
Cite
@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}
}
Acknowledgments
We would like to thank the authors and developers of the following projects, this project is built upon these great open-sourced projects.
Contact
- If you have any questions, please report issues on GitHub.