Summary of Fedrsu: Federated Learning For Scene Flow Estimation on Roadside Units, by Shaoheng Fang et al.
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
by Shaoheng Fang, Rui Ye, Wenhao Wang, Zuhong Liu, Yuxiao Wang, Yafei Wang, Siheng Chen, Yanfeng Wang
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Federated Roadside Unit (FedRSU) framework leverages vast amounts of data from multiple roadside units to improve self-supervised scene flow estimation in autonomous vehicles. FedRSU employs a recurrent training paradigm, where each RSU predicts scene flows based on its subsequent observations, and federated learning enables collaborative model training while keeping data local and private. The proposed framework improves model performance and provides a comprehensive benchmark for various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedRSU is a new way to use data from roadside units to make autonomous vehicles safer. This system collects lots of information from many RSUs and uses it to improve predictions about what’s happening on the road. It does this by having each RSU predict what will happen next, based on its own sensors, and then combining those predictions with others from other RSUs. This makes the system better at predicting things like where cars are going to go or when a pedestrian is about to step into the street. The data collected from many RSUs can be used to train models that get better over time. |
Keywords
* Artificial intelligence * Federated learning * Self supervised
