CarlaFLCAV is an open-source FLCAV simulation platform based on CARLA simulator that supports:

• Multi-modal dataset generation: Including point-cloud, image, radar data with associated calibration, synchronization, and annotation

• Training and inference: Examples for CAV perception, including object detection, traffic sign detection, and weather classification

• Various FL frameworks: FedAvg, device selection, noisy aggregation, parameter selection, distillation, and personalization

• Optimization based modules: Network resource and road sensor pose optimization.

• Ubuntu 20.04
• Python 3.8
• CARLA 0.9.13
• CUDA 11.3 (Nvidia Driver 470)
• Pytorch 1.10.0

CarlaFLCAV can reproduce results in the following papers:

@article{FLCAV,
  title={Federated deep learning meets autonomous vehicle perception: Design and verification},
  author={Shuai Wang and Chengyang Li and Qi Hao and Chengzhong Xu and Derrick Wing Kwan Ng and Yonina C. Eldar and H. Vincent Poor},
  journal={arXiv preprint arXiv:2206.01748},
  year={2022}
}

NOTE：This is a Test version. Final version will be released after paper acceptance.

Shuai Wang

Chengyang Li