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Awesome-LLM-for-Autonomous-Driving-Resources

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This is a collection of research papers about LLM-for-Autonomous-Driving(LLM4AD). And the repository will be continuously updated to track the frontier of LLM4AD. Maintained by SJTU-ReThinklab.

Welcome to follow and star! If you find any related materials could be helpful, feel free to contact us (yangzhenjie@sjtu.edu.cn or jiaxiaosong@sjtu.edu.cn) or make a PR.

Citation

Our survey paper is at https://arxiv.org/abs/2311.01043 which includes more detailed discussions and will be continuously updated. The latest version was updated on August 12, 2024.

If you find our repo is helpful, please consider cite it.

@misc{yang2023survey,
      title={LLM4Drive: A Survey of Large Language Models for Autonomous Driving}, 
      author={Zhenjie Yang and Xiaosong Jia and Hongyang Li and Junchi Yan},
      year={2023},
      eprint={2311.01043},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

Table of Contents

Overview of LLM4AD

LLM-for-Autonomous-Driving (LLM4AD) refers to the application of Large Language Models(LLMs) in autonomous driving. We divide existing works based on the perspective of applying LLMs: planning, perception, question answering, and generation.

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Motivation of LLM4AD

The orange circle represents the ideal level of driving competence, akin to that possessed by an experienced human driver. There are two main methods to acquire such proficiency: one, through learning-based techniques within simulated environments; and two, by learning from offline data through similar methodologies. It’s important to note that due to discrepancies between simulations and the real-world, these two domains are not fully the same, i.e. sim2real gap. Concurrently, offline data serves as a subset of real-world data since it’s collected directly from actual surroundings. However, it is difficult to fully cover the distribution as well due to the notorious long-tailed nature of autonomous driving tasks. The final goal of autonomous driving is to elevate driving abilities from a basic green stage to a more advanced blue level through extensive data collection and deep learning.

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ICLR 2024 Open Review

<details close> <summary>Toggle</summary>
format:
- [title](paper link) [links]
  - task
  - keyword
  - code or project page
  - datasets or environment or simulator
  - summary
</details>

Papers

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format:
- [title](paper link) [links]
  - author1, author2, and author3...
  - publisher
  - task
  - keyword
  - code or project page
  - datasets or environment or simulator
  - publish date
  - summary
  - metrics
</details>

WorkShop

<details open> <summary>Toggle</summary> </details>

Datasets

<details open> <summary>Toggle</summary>
format:
- [title](dataset link) [links]
  - author1, author2, and author3...
  - keyword
  - experiment environments or tasks
</details>

License

Awesome LLM for Autonomous Driving Resources is released under the Apache 2.0 license.