Yunsong Zhou, Naisheng Ye, William Ljungbergh, Tianyu Li, Jiazhi Yang, Zetong Yang, Hongzi Zhu, Christoffer Petersson, and Hongyang Li
- Presented by OpenDriveLab
- 📬 Primary contact: Yunsong Zhou ( zhouyunsong2017@gmail.com )
- arXiv paper | Blog TODO | Slides
🔥 Nexus is a noise-decoupled prediction pipeline designed for adaptive driving scene generation, ensuring both timely reaction⏲️ and goal-directed control🥅.
🌟 Nexus can generate realistic safety-critical driving scenarios by flexibly controlling the future state of a scene, with the assistance of NeRF.
[2024/04]Nexus paper released.[2025/04]Nexus code and data initially released.
- Guidance tutorial
- Training code
- Nexus & checkpoint
- Initial repo & paper
Nexus-Data is induced from real-world scenarios, in which we can obtain real-world map topology and layout. It also includes hazardous driving behaviors through interactions introduced by adversarial traffic generation. The safety-critical scenarios (on nuPlan dataset) can be obtained through this 🔗data link.
All assets and code in this repository are under the Apache 2.0 license unless specified otherwise. The data is under CC BY-NC-SA 4.0. Please consider citing our paper and project if they help your research.
@article{zhou2024decoupled,
title={Decoupled Diffusion Sparks Adaptive Scene Generation},
author={Zhou, Yunsong and Ye, Naisheng and Ljungbergh, William and Li, Tianyu and Yang, Jiazhi and Yang, Zetong and Zhu, Hongzi and Petersson, Christoffer and Li, Hongyang},
journal={arXiv preprint arXiv:2504.10485},
year={2025}
}We acknowledge all the open-source contributors for the following projects to make this work possible:
