This repository serves as a curated, up-to-date list of research papers on world models for autonomous driving. It aims to provide a comprehensive overview of the field for researchers and practitioners in autonomous systems, offering insights into various approaches for predictive modeling, simulation, and planning in driving scenarios.

How It Works

The repository categorizes papers by conference (CVPR, ICLR, NeurIPS, ECCV, AAAI, ICRA) and year, starting from 2018. It also includes sections for general world model papers, surveys, workshops, and related repositories. Each entry typically links to the paper, code, and sometimes a project website or dataset, facilitating easy access to the underlying research.

Quick Start & Requirements

This repository is a curated list and does not require installation or execution. It serves as a reference guide to external research papers and their associated codebases.

Highlighted Details

• Comprehensive coverage of major AI conferences and workshops related to autonomous driving.
• Includes foundational papers and recent advancements in generative AI, reinforcement learning, and self-supervised learning for world models.
• Features links to code repositories, enabling direct experimentation with published methods.
• Highlights key research groups and their contributions, such as NYU Learning Systems Laboratory and Yann LeCun's group.

Maintenance & Community

The repository is actively maintained to include the latest publications. It references several related "Awesome" lists and community resources for further exploration.

Licensing & Compatibility

This repository itself is a list of links and does not have a specific license. The licensing of the linked papers and code repositories varies and must be checked individually.

Limitations & Caveats

The repository is a curated list and does not provide direct access to the models themselves, nor does it offer any form of benchmarking or comparative analysis of the listed approaches. Users must consult the individual papers for detailed information on model performance, requirements, and limitations.