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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of awesome LLM/VLM/VLA/World Model for Autonomous Driving(LLM4AD) resources (continually updated)
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Today
+1 stars today
This week
+3 stars this week
This month
+13 stars this month
Momentum
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growth rate 0.16%/day
# Awesome-LLM-for-Autonomous-Driving-Resources [](https://github.com/sindresorhus/awesome)  [](https://github.com/Thinklab-SJTU/Awesome-LLM4AD/blob/main/LICENSE) This is a collection of research papers about **LLM-for-Autonomous-Driving(LLM4AD)**. The repository will be continuously updated to track the frontier of LLM4AD (Large Language Models for Autonomous Driving), which encompasses VLM4AD (Vision-Language Models for AD) and VLA4AD (Vision-Language-Action models for AD) as integral components of this unified paradigm. *Maintained by SJTU-ReThinklab.* Welcome to follow and star! If you find any related materials could be helpful, feel free to contact us ([email protected] or [email protected]) 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. If you find our repo is helpful, please consider cite it. ```BibTeX @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 - [Awesome LLM-for-Autonomous-Driving(LLM4AD)](#awesome-llm-for-autonomous-driving-resources) - [Table of Contents](#table-of-contents) - [Overview of LLM4AD](#overview-of-llm4ad) - [Papers](#papers) - [Datasets](#datasets) - [Citation](#citation) - [License](#license) ## 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.  ## 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.  ## Papers <details open> <summary>Toggle</summary> ``` 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 ``` - [SafeDriveVLA: Navigation-Conditioned World Model Dreaming for Conflict-Aware End-to-End Autonomous Driving](https://arxiv.org/abs/2610.20609) - Shaoyuan Xie, Zihan Zhang, Jingxuan Wang, Jiashu Qu, Xiaoqing Liang, Lingdong Kong, Junchi Lu, Henrik I. Christensen, Qi Alfred Chen - Publish Date: 2026.10.14 - Task: End-to-End - Datasets: [Bench2Drive](https://github.com/Thinklab-SJTU/Bench2Drive) - Summary： - Proposes SafeDriveVLA, the first driving VLA that predicts a three-level navigation-complia
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Zhenjie Yang · Postdoc@HKU, PhD@SJTU · Hong Kong
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9c792325a1bea87b, topic:vision-language-model, readme:vision-language
matched fp:9c792325a1bea87b, topic:large-language-models