Note
The documentation is currently incomplete. At this stage, the repository primarily provides a working pipeline for testing Baidu Apollo using a lightweight traffic simulator.
The framework has been tested and shown to be stable on the maintainer’s server setup.
Please feel free to join our Discord for quick discussions, and open GitHub issues for questions, bug reports, or suggestions.Contributions are very welcome — including documentation improvements, bug fixes, or functionality extensions.
Any feedback or support from the community is sincerely appreciated.
ApolloSimFuzz integrates Baidu Apollo with TrafficSandbox, a lightweight traffic simulation framework, to support flexible, scalable, and closed-loop testing of Apollo’s decision-making functionalities in lightweight simulation environments.
The framework is designed for simulation-based testing and fuzzing, where Apollo interacts with a traffic simulator through perfect perception results and control commands. This enables systematic evaluation of decision-making under diverse traffic scenarios without relying on full-stack, high-fidelity simulators.
Before installation, please ensure the following dependencies are available on your system:
⚠️ GPU support is required for running Apollo modules.
- Installation Guide
👉 Installation
This project currently provides basic testing functionality (e.g., random testing) and is under active development.
Planned improvements include:
- Providing comprehensive documentation and usage examples
- Refining minor components to improve robustness and stability
For questions, contributions, or collaboration inquiries:
- Maintainer: Mingfei Cheng
- Email: snowbirds.mf@gmail.com
If you find this project useful for research or development, please consider supporting it via GitHub Sponsors.
If you use Drivora / ApolloSimFuzz in your research, please cite the following papers:
@article{cheng2026drivora,
title = {Drivora: A Unified and Extensible Infrastructure for Search-based Autonomous Driving Testing},
author = {Cheng, Mingfei and Briand, Lionel and Zhou, Yuan},
journal = {arXiv preprint arXiv:2601.05685},
year = {2026}
}
@inproceedings{cheng2025decictor,
title = {Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems},
author = {Cheng, Mingfei and Xie, Xiaofei and Zhou, Yuan and Wang, Junjie and Meng, Guozhu and Yang, Kairui},
booktitle = {Proceedings of the 47th IEEE/ACM International Conference on Software Engineering (ICSE)},
pages = {1--13},
year = {2025},
organization = {IEEE}
}
@inproceedings{cheng2023behavexplor,
title = {Behavexplor: Behavior Diversity Guided Testing for Autonomous Driving Systems},
author = {Cheng, Mingfei and Zhou, Yuan and Xie, Xiaofei},
booktitle = {Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)},
pages = {488--500},
year = {2023}
}📌 More related references are listed in reference.bib.
This project is released under the MIT License. See LICENSE for details.