# LMD0311/Awesome-World-Model

Collect some World Models for Autonomous Driving (and Robotic, etc.) papers.

Repository: https://github.com/LMD0311/Awesome-World-Model
Canonical: https://ross.abutalabs.com/products/awesome-world-model
Homepage: https://arxiv.org/abs/2502.10498
License Family: other
Topics: autonomous-driving, autonomous-vehicles, computer-vision, world-model, future-predict, artificial-intelligence, artificial-intelligence-algorithms, awesome, deep-learning, robotics
Last push: 2026-08-17T08:52:48+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 69
- inputs: {"age_days": 974, "days_push": 16, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2225, forks 86 (observed 2026-08-28T04:06:28.078127+00:00)

## What it is
A curated awesome-list collecting papers on World Models for Autonomous Driving and Robotics, maintained as a supplement to a published survey (arXiv:2502.10498). It tracks, records, and benchmarks recent driving world model methods across video, point cloud, occupancy, latent feature, and traffic map modalities.

## Use cases
- find papers on world models for autonomous driving
- survey driving world model methods for a literature review
- track recent research on future scene prediction for self-driving
- compare world model approaches across video, point cloud, and occupancy modalities
- find datasets and metrics for evaluating driving world models
- research world models for robotics and embodied AI

## When to choose
- you need a comprehensive, continuously updated bibliography of driving world model papers
- you are writing a literature review or survey on world models for autonomous driving
- you want to benchmark or compare world model methods across modalities

## When to avoid
- you need runnable code or a software library rather than a paper list
- you need a single maintained implementation instead of research references
- you are looking for general world model research outside driving and robotics

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, machine-learning, computer-vision
- domain: autonomous-vehicles, artificial-intelligence, deep-learning, robotics, computer-vision, awesome-lists
- platform: cross-platform
- tags: awesome-list, world-models, survey, paper-collection, future-prediction, benchmarking

## Member repositories
- LMD0311/Awesome-World-Model (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:28.078127+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 2026-08-30T02:44:54.754571+00:00, confidence not recorded.
  - readme: https://github.com/LMD0311/Awesome-World-Model (fetched 2026-08-28T04:06:28.078127+00:00, sha 5dc9104b5022)
  - homepage: https://arxiv.org/abs/2502.10498 (fetched 2026-08-29T10:25:48.957743+00:00, sha 2b73b2881ed3)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T10:25:48.967060+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T10:25:48.971306+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T10:25:48.993941+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T10:25:48.968918+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.