Fetching the paper…
Reading the bibliography…
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in IEEE International Conference on Computer Vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Q. Chen, X. Ma, S. Tang, J. Guo, Q. Yang, and S. Fu, “F-cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,” in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing , 2019, pp. 88–100
2019
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y.-C. Liu, J. Tian, N. Glaser, and Z. Kira, “When2com: Multi-agent perception via communication graph grouping,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , 2020, pp. 4106–4115
2020
Earlier work this paper cites.
T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2vnet: Vehicle-to-vehicle communication for joint perception and prediction,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 605–621
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y. Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning distilled collaboration graph for multi-agent perception,” Advances in Neural Information Processing Systems , vol. 34, pp. 29 541–29 552, 2021
2021
Earlier work this paper cites.
M. A. Khan, S. Ghosh, S. A. Busari, K. M. S. Huq, T. Dagiuklas, S. Mumtaz, M. Iqbal, and J. Rodriguez, “Robust, resilient and reliable architecture for v2x communications,” IEEE Transactions on Intelligent Transportation Systems , vol. 22, no. 7, pp. 4414–4430, 2021
2021
Earlier work this paper cites.
A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré, “Combining recurrent, convolutional, and continuous-time models with linear state space layers,” Advances in neural information processing systems , vol. 34, pp. 572–585, 2021
2021
Earlier work this paper cites.
R. Xu, Y. Guo, X. Han, X. Xia, H. Xiang, and J. Ma, “Opencda: an open cooperative driving automation framework integrated with co-simulation,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 1155–1162
2021
Earlier work this paper cites.
R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2583–2589
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,” in European conference on computer vision . Springer, 2022, pp. 107–124
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,” Advances in neural information processing systems , vol. 35, pp. 4874–4886, 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
H. Xiang, R. Xu, and J. Ma, “Hm-vit: Hetero-modal vehicle-to-vehicle cooperative perception with vision transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 284–295
2023
Cited alongside, same era.
Z. Song, F. Wen, H. Zhang, and J. Li, “A cooperative perception system robust to localization errors,” in 2023 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2023, pp. 1–6
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Dao, D. Y. Fu, K. K. Saab, A. W. Thomas, A. Rudra, and C. Ré, “Hungry hungry hippos: Towards language modeling with state space models,” in Proceedings of the 11th International Conference on Learning Representations (ICLR) , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song et al. , “V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 712–13 722
2023
Cited alongside, same era.
Y. Lu, Q. Li, B. Liu, M. Dianati, C. Feng, S. Chen, and Y. Wang, “Robust collaborative 3d object detection in presence of pose errors,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4812–4818
2023
Cited alongside, same era.
Z. Meng, X. Xia, R. Xu, W. Liu, and J. Ma, “Hydro-3d: Hybrid object detection and tracking for cooperative perception using 3d lidar,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Cited alongside, same era.
K. Yang, D. Yang, J. Zhang, M. Li, Y. Liu, J. Liu, H. Wang, P. Sun, and L. Song, “Spatio-temporal domain awareness for multi-agent collaborative perception,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 23 383–23 392
2023
Cited alongside, same era.
R. Xu, W. Chen, H. Xiang, X. Xia, L. Liu, and J. Ma, “Model-agnostic multi-agent perception framework,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1471–1478
2023
Cited alongside, same era.
R. Xu, J. Li, X. Dong, H. Yu, and J. Ma, “Bridging the domain gap for multi-agent perception,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 6035–6042
2023
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. Ren, Z. Lei, Z. Wang, M. Dianati, Y. Wang, S. Chen, and W. Zhang, “Interruption-aware cooperative perception for v2x communication-aided autonomous driving,” IEEE Transactions on Intelligent Vehicles , 2024
2024
Closest in time.
J. Li, B. Li, X. Liu, J. Fang, F. Juefei-Xu, Q. Guo, and H. Yu, “Advgps: Adversarial gps for multi-agent perception attack,” in IEEE International Conference on Robotics and Automation , 2024
2024
Closest in time.
J. Li, B. Li, X. Liu, R. Xu, J. Ma, and H. Yu, “Breaking data silos: Cross-domain learning for multi-agent perception from independent private sources,” in IEEE International Conference on Robotics and Automation , 2024
2024
Closest in time.
J. Li, R. Xu, X. Liu, B. Li, Q. Zou, J. Ma, and H. Yu, “S2r-vit for multi-agent cooperative perception: Bridging the gap from simulation to reality,” in IEEE International Conference on Robotics and Automation , 2024
2024
Closest in time.
2024
Closest in time.