Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow
Authors: Sizhe Wei, Yuxi Wei, Yue Hu, Yifan Lu, Yiqi Zhong, Siheng Chen, Ya Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on both IRV2V and the real-world dataset DAIR-V2X show that Co BEVFlow consistently outperforms other baselines and is robust in extremely asynchronous settings. The code is available at https://github.com/Media Brain-SJTU/Co BEVFlow. |
| Researcher Affiliation | Collaboration | 1 Cooperative Medianet Innovation Center, Shanghai Jiao Tong University 2 University of Southern California 3 Shanghai AI Laboratory 1 EMAIL EMAIL 2 EMAIL |
| Pseudocode | No | The paper describes the system architecture and processes in text and with equations, but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | The code is available at https://github.com/Media Brain-SJTU/Co BEVFlow. |
| Open Datasets | Yes | To facilitate research on asynchrony for collaborative perception, we simulate the first collaborative perception dataset with different temporal asynchronies based on CARLA [39], named IRregular V2V(IRV2V). ... DAIR-V2X. DAIR-V2X [14] is a real-world collaborative perception dataset. |
| Dataset Splits | Yes | We have split the dataset into training, validation, and testing sets, which contain 5,445, 994, and 2,010 samples, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Point Pillars[40] and adopting settings from Co Align[17] for the backbone, but does not provide specific version numbers for software dependencies like Python, PyTorch, CUDA, etc. |
| Experiment Setup | Yes | We conduct training for a total of 60 epochs, starting with an initial learning rate of 2e-3. Subsequently, at the 10th and 20th epochs, the learning rate decreases to 10% of its previous value. For IRV2V dataset, we set the lidar range as x [ 140.8, +140.8]m, y [ 40, +40]m. The voxel size is h = w = 0.4m. The feature map s size is H = 200, W = 704. For DAIR-V2X dataset, we set the lidar range as x [ 100.8, +100.8]m, y [ 40, +40]m. The voxel size is h = w = 0.4m. The feature map s size is H = 200, W = 504. |