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Infrastructure sensors installed at elevated positions offer a broader perception range and encounter fewer occlusions.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4490–4499
2018
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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
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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
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C. Schöller, V. Aravantinos, F. Lay, and A. Knoll, “What the constant velocity model can teach us about pedestrian motion prediction,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1696–1703, 2020
2020
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H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
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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
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A. Caillot, S. Ouerghi, P. Vasseur, R. Boutteau, and Y. Dupuis, “Survey on cooperative perception in an automotive context,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 9, pp. 14 204–14 223, 2022
2022
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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
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Y. Li, D. Ma, Z. An, Z. Wang, Y. Zhong, S. Chen, and C. Feng, “V2x-sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 914–10 921, 2022
2022
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J. Xu, J. Zhong, Y. Huang, and Y. Liu, “Unf-slam: Unsupervised feature extraction network for visual-laser fusion slam,” in 2022 IEEE International Conference on Unmanned Systems (ICUS) . IEEE, 2022, pp. 284–291
2022
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Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” in European conference on computer vision . Springer, 2022, pp. 1–18
2022
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H. Yu, Y. Luo, M. Shu, Y. Huo, Z. Yang, Y. Shi, Z. Guo, H. Li, X. Hu, J. Yuan et al. , “Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 21 361–21 370
2022
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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
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R. Mao, J. Guo, Y. Jia, Y. Sun, S. Zhou, and Z. Niu, “Dolphins: Dataset for collaborative perception enabled harmonious and interconnected self-driving,” in Proceedings of the Asian Conference on Computer Vision , 2022, pp. 4361–4377
2022
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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
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Y. Zhou, J. Xiao, Y. Zhou, and G. Loianno, “Multi-robot collaborative perception with graph neural networks,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 2289–2296, 2022
2022
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Y. Han, H. Zhang, H. Li, Y. Jin, C. Lang, and Y. Li, “Collaborative perception in autonomous driving: Methods, datasets, and challenges,” IEEE Intelligent Transportation Systems Magazine , 2023
2023
Cited alongside, same era.
Q. Zhang, X. Zhang, R. Zhu, F. Bai, M. Naserian, and Z. M. Mao, “Robust real-time multi-vehicle collaboration on asynchronous sensors,” in Proceedings of the 29th Annual International Conference on Mobile Computing and Networking , 2023, pp. 1–15
2023
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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
S. Fan, H. Yu, W. Yang, J. Yuan, and Z. Nie, “Quest: Query stream for vehicle-infrastructure cooperative perception,” in 2024 IEEE international conference on robotics and automation (ICRA) , 2024
2024
Closest in time.
H. Yu, Y. Tang, E. Xie, J. Mao, P. Luo, and Z. Nie, “Flow-based feature fusion for vehicle-infrastructure cooperative 3d object detection,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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M.-Q. Dao, J. S. Berrio, V. Frémont, M. Shan, E. Héry, and S. Worrall, “Practical collaborative perception: A framework for asynchronous and multi-agent 3d object detection,” IEEE Transactions on Intelligent Transportation Systems , 2024
2024
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D. Yang, K. Yang, Y. Wang, J. Liu, Z. Xu, R. Yin, P. Zhai, and L. Zhang, “How2comm: Communication-efficient and collaboration-pragmatic multi-agent perception,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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Cited alongside, same era.
T. Wang, G. Chen, K. Chen, Z. Liu, B. Zhang, A. Knoll, and C. Jiang, “Umc: A unified bandwidth-efficient and multi-resolution based collaborative perception framework,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8187–8196
2023
Cited alongside, same era.
K. Yang, D. Yang, J. Zhang, H. Wang, P. Sun, and L. Song, “What2comm: Towards communication-efficient collaborative perception via feature decoupling,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 7686–7695
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.
2023
Cited alongside, same era.
H. Yu, W. Yang, H. Ruan, Z. Yang, Y. Tang, X. Gao, X. Hao, Y. Shi, Y. Pan, N. Sun et al. , “V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5486–5495
2023
Cited alongside, same era.
J. Xu, W. Shao, Y. Xu, W. Wang, J. Li, and H. Wang, “A risk probability predictor for effective downstream planning tasks,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2023, pp. 5416–5422
2023
Cited alongside, same era.
S. Wang, Y. Liu, T. Wang, Y. Li, and X. Zhang, “Exploring object-centric temporal modeling for efficient multi-view 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3621–3631
2023
Cited alongside, same era.
H. Wang, H. Tang, S. Shi, A. Li, Z. Li, B. Schiele, and L. Wang, “Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view representation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6792–6802
2023
Cited alongside, same era.
S. Wei, Y. Wei, Y. Hu, Y. Lu, Y. Zhong, S. Chen, and Y. Zhang, “Asynchrony-robust collaborative perception via bird’s eye view flow,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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Y. Liu, Q. Huang, R. Li, X. Chen, Z. Zhao, S. Zhao, Y. Zhu, and H. Zhang, “Select2col: Leveraging spatial-temporal importance of semantic information for efficient collaborative perception,” 2024
2024
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2024
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T. Zhu, J. Leng, J. Zhong, Z. Zhang, and C. Sun, “Lanemapnet: Lane network recognization and hd map construction using curve region aware temporal bird’s-eye-view perception,” in 2024 IEEE Intelligent Vehicles Symposium (IV) , 2024, pp. 2168–2175
2024
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X. Liu, J. Zhong, and C. Sun, “Bevmamba: Time sequence dense bird’s-eye-view perception modeling with state space model,” TechRxiv , 2024
2024
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2024
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2024
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2024
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R. Hao, S. Fan, Y. Dai, Z. Zhang, C. Li, Y. Wang, H. Yu, W. Yang, Y. Jirui, and Z. Nie, “Rcooper: A real-world large-scale dataset for roadside cooperative perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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C. Lin, D. Tian, X. Duan, J. Zhou, D. Zhao, and D. Cao, “V2vformer: Vehicle-to-vehicle cooperative perception with spatial-channel transformer,” IEEE Transactions on Intelligent Vehicles , 2024
2024
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