CarlaFLCAV is an open-source FLCAV simulation platform based on CARLA simulator that supports:
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Multi-modal dataset generation: Including point-cloud, image, radar data with associated calibration, synchronization, and annotation
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Training and inference: Examples for CAV perception, including object detection, traffic sign detection, and weather classification
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Various FL frameworks: FedAvg, device selection, noisy aggregation, parameter selection, distillation, and personalization
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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.