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Collaborative Perception in Autonomous Driving Survey

This repo is constructed for collecting and categorizing papers about collaborative perception according to our ITSM survey paper: Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges [arXiv] [ITSM] [Zhihu]

Methods

Methods for Ideal Scenarios

  • Raw data fusion
  • Customized communication mechanism
  • Feature fusion
  • Customized loss function
  • Output fusion

👉 View details in Methods for Ideal Scenarios

Methods for Real-world Issues

  • Localization errors
  • Communication issues
  • Model or task discrepancies
  • Privacy and security issues

👉 View details in Methods for Real-World Issues

Datasets

  • Real-world or Simulator
  • V2V or V2I

👉 View details in Datasets Summary

Challenges

  • Transmission Efficiency in Collaborative Perception
  • Collaborative Perception in Complex Scenes
  • Federated Learning-based Collaborative Perception
  • Collaborative Perception with Low Labeling Dependence

👉 View details in New Trends

Citation

If you find this work useful, please cite our paper:

@article{han2023collaborative,
  author={Han, Yushan and Zhang, Hui and Li, Huifang and Jin, Yi and Lang, Congyan and Li, Yidong},
  journal={IEEE Intelligent Transportation Systems Magazine}, 
  title={Collaborative Perception in Autonomous Driving: Methods, Datasets, and Challenges}, 
  year={2023},
  volume={15},
  number={6},
  pages={131-151},
  doi={10.1109/MITS.2023.3298534}}