# Thinklab-SJTU/Awesome-LLM4AD

A curated list of awesome LLM/VLM/VLA/World Model for Autonomous Driving(LLM4AD) resources (continually updated)

Repository: https://github.com/Thinklab-SJTU/Awesome-LLM4AD
Canonical: https://ross.abutalabs.com/products/awesome-llm4ad
License: Apache-2.0
License Family: permissive
Topics: large-language-models, vision-language-action-model, vision-language-model, world-model
Last push: 2026-06-22T14:29:16+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 75
- inputs: {"age_days": 1062, "days_push": 72, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1892, forks 112 (observed 2026-08-28T04:05:50.343170+00:00)

## What it is
A curated, continuously updated list of research papers, datasets, and resources on applying Large Language Models (and VLM/VLA/World Models) to autonomous driving. Maintained by SJTU-ReThinklab alongside their LLM4Drive survey paper.

## Use cases
- find papers on LLMs for autonomous driving
- research vision-language-action models for driving
- survey datasets for LLM4AD research
- track the state of the art in LLM-based driving planners
- prepare a literature review on autonomous driving with foundation models
- find world model papers for autonomous vehicles

## When to choose
- you need a comprehensive, categorized reading list of LLM4AD research
- you are starting research on LLM/VLM/VLA applications in autonomous driving
- you want a companion resource to the LLM4Drive survey paper

## When to avoid
- you need runnable code or a software library rather than a paper list
- you need production tools for autonomous driving systems
- you need non-driving LLM resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: autonomous-vehicles, large-language-models, artificial-intelligence, awesome-lists
- platform: cross-platform
- tags: awesome-list, llm4ad, vision-language-models, vision-language-action, world-model, research-papers, survey

## Member repositories
- Thinklab-SJTU/Awesome-LLM4AD (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:50.343170+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:12:56.910816+00:00, confidence not recorded.
  - readme: https://github.com/Thinklab-SJTU/Awesome-LLM4AD (fetched 2026-08-28T04:05:50.343170+00:00, sha 24e56c9ba6b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.