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Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in
2012
Earlier work this paper cites.
D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker, “Recent development and applications of sumo-simulation of urban mobility,”
2012
Earlier work this paper cites.
E. Strigel, D. Meissner, F. Seeliger, B. Wilking, and K. Dietmayer, “The ko-per intersection laserscanner and video dataset,” in
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,”
2015
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in
2017
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in
2018
Earlier work this paper cites.
Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving,” in
2019
Earlier work this paper cites.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in
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
2019
Earlier work this paper cites.
Z. Tang, M. Naphade, M.-Y. Liu, X. Yang, S. Birchfield, S. Wang, R. Kumar, D. Anastasiu, and J.-N. Hwang, “Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification,” in
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Q. Chen, S. Tang, Q. Yang, and S. Fu, “Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,” in
2019
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine,
2020
Earlier work this paper cites.
Y. Li, L. Ma, Z. Zhong, F. Liu, M. A. Chapman, D. Cao, and J. Li, “Deep learning for lidar point clouds in autonomous driving: A review,”
2020
Earlier work this paper cites.
Y. Li and J. Ibanez-Guzman, “Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems,”
2020
Earlier work this paper cites.
G. Velasco-Hernandez, J. Barry, J. Walsh,
2020
Earlier work this paper cites.
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,”
2020
Earlier work this paper cites.
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,”
2020
Earlier work this paper cites.
M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, and F. Heide, “Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather,” in
2020
Earlier work this paper cites.
A. Palffy, J. Dong, J. F. Kooij, and D. M. Gavrila, “Cnn based road user detection using the 3d radar cube,”
2020
Earlier work this paper cites.
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
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
2020
Earlier work this paper cites.
E. Arnold, M. Dianati, R. de Temple, and S. Fallah, “Cooperative perception for 3d object detection in driving scenarios using infrastructure sensors,”
2020
Earlier work this paper cites.
E. Arnold, S. Mozaffari, and M. Dianati, “Fast and robust registration of partially overlapping point clouds,”
2021
Earlier work this paper cites.
S. Malik, M. A. Khan, and H. El-Sayed, “Collaborative autonomous driving—a survey of solution approaches and future challenges,”
2021
Earlier work this paper cites.
Y. Yuan and M. Sester, “Comap: A synthetic dataset for collective multi-agent perception of autonomous driving,”
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
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,”
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu,
2022
Earlier work this paper cites.
S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in vision: A survey,”
2022
Earlier work this paper cites.
M. Yang, W. Liu, Z. Liu, C. Cai, Y. Wang, and J. Yang, “Binocular vision-based method used for determining the static and dynamic parameters of the long-stroke shakers in low-frequency vibration calibration,”
2022
Earlier work this paper cites.
L. Zheng, Z. Ma, X. Zhu, B. Tan, S. Li, K. Long, W. Sun, S. Chen, L. Zhang, M. Wan,
2022
Earlier work this paper cites.
Z. Song, Y. Zhang, Y. Liu, K. Yang, and M. Sun, “Msfyolo: Feature fusion-based detection for small objects,”
2022
Earlier work this paper cites.
Y. Li, A. W. Yu, T. Meng, B. Caine, J. Ngiam, D. Peng, J. Shen, Y. Lu, D. Zhou, Q. V. Le,
2022
Earlier work this paper cites.
L. Wang, X. Zhang, B. Xv, J. Zhang, R. Fu, X. Wang, L. Zhu, H. Ren, P. Lu, J. Li,
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
2022
Earlier work this paper cites.
Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,”
2022
Earlier work this paper cites.
H. Yu, Y. Luo, M. Shu, Y. Huo, Z. Yang, Y. Shi, Z. Guo, H. Li, X. Hu, J. Yuan,
2022
Earlier work this paper cites.
X. Zhang, Z. Li, Y. Gong, D. Jin, J. Li, L. Wang, Y. Zhu, and H. Liu, “Openmpd: An open multimodal perception dataset for autonomous driving,”
2022
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
Earlier work this paper cites.
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,”
2022
Earlier work this paper cites.
C. Creß, W. Zimmer, L. Strand, M. Fortkord, S. Dai, V. Lakshminarasimhan, and A. Knoll, “A9-dataset: Multi-sensor infrastructure-based dataset for mobility research,” in
2022
Earlier work this paper cites.
H. Wang, X. Zhang, Z. Li, J. Li, K. Wang, Z. Lei, and R. Haibing, “Ips300+: a challenging multi-modal data sets for intersection perception system,” in
2022
Earlier work this paper cites.
X. Ye, M. Shu, H. Li, Y. Shi, Y. Li, G. Wang, X. Tan, and E. Ding, “Rope3d: The roadside perception dataset for autonomous driving and monocular 3d object detection task,” in
2022
Earlier work this paper cites.
S. Busch, C. Koetsier, J. Axmann, and C. Brenner, “Lumpi: The leibniz university multi-perspective intersection dataset,” in
2022
Earlier work this paper cites.
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
2022
Cited alongside, same era.
X. Hu, Y. Cao, T. Tang, and Y. Sun, “Data-driven technology of fault diagnosis in railway point machines: Review and challenges,”
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Yuan, H. Cheng, and M. Sester, “Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving,”
2022
Cited alongside, same era.
Springer, 2023
R. Fan, S. Guo, and M. J. Bocus, · 2023
Cited alongside, same era.
Y. Wang, Z. Han, Y. Xing, S. Xu, and J. Wang, “A survey on datasets for the decision making of autonomous vehicles,”
2024
Later among the works it cites.
M. Liu, E. Yurtsever, J. Fossaert, X. Zhou, W. Zimmer, Y. Cui, B. L. Zagar, and A. C. Knoll, “A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,”
2024
Later among the works it cites.
M. Yazgan, T. Graf, M. Liu, T. Fleck, and J. M. Zöllner, “A survey on intermediate fusion methods for collaborative perception categorized by real world challenges,” in
2024
Later among the works it cites.
B. Gao, J. Liu, H. Zou, J. Chen, L. He, and K. Li, “Vehicle-road-cloud collaborative perception framework and key technologies: A review,”
2024
Later among the works it cites.
S. Teufel, J. Gamerdinger, J.-P. Kirchner, G. Volk, and O. Bringmann, “Collective perception datasets for autonomous driving: A comprehensive review,” in
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J. Mao, S. Shi, X. Wang, and H. Li, “3d object detection for autonomous driving: A comprehensive survey,”
2023
Cited alongside, same era.
Y. Gong, J. Lu, W. Liu, Z. Li, X. Jiang, X. Gao, and X. Wu, “Sifdrivenet: Speed and image fusion for driving behavior classification network,”
2023
Cited alongside, same era.
L. Wang, Z. Song, X. Zhang, C. Wang, G. Zhang, L. Zhu, J. Li, and H. Liu, “Sat-gcn: Self-attention graph convolutional network-based 3d object detection for autonomous driving,”
2023
Cited alongside, same era.
Z. Song, H. Wei, L. Bai, L. Yang, and C. Jia, “Graphalign: Enhancing accurate feature alignment by graph matching for multi-modal 3d object detection,” in
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,
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,
2023
Cited alongside, same era.
2024
Later among the works it cites.
M. Yazgan, M. V. Akkanapragada, and J. M. Zöllner, “Collaborative perception datasets in autonomous driving: A survey,” in
2024
Later among the works it cites.
X. Zhu, H. Sheng, S. Cai, B. Deng, S. Yang, Q. Liang, K. Chen, L. Gao, J. Song, and J. Ye, “Roscenes: A large-scale multi-view 3d dataset for roadside perception,” in
2024
Later among the works it cites.
Y. Li, Z. Li, N. Chen, M. Gong, Z. Lyu, Z. Wang, P. Jiang, and C. Feng, “Multiagent multitraversal multimodal self-driving: Open mars dataset,” in
2024
Later among the works it cites.
2024
Later among the works it cites.
C. Ma, L. Qiao, C. Zhu, K. Liu, Z. Kong, Q. Li, X. Zhou, Y. Kan, and W. Wu, “Holovic: Large-scale dataset and benchmark for multi-sensor holographic intersection and vehicle-infrastructure cooperative,” in
2024
Later among the works it cites.
H. Zhu, Y. Wang, Q. Kong, Y. Wei, X. Xia, B. Deng, R. Xiong, and Y. Wang, “Otvic: A dataset with online transmission for vehicle-to-infrastructure cooperative 3d object detection,” in
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Zhou, H. Xiang, Z. Zheng, S. Z. Zhao, M. Lei, Y. Zhang, T. Cai, X. Liu, J. Liu, M. Bajji,
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
T. Zhao, L. Yang, Y. Xie, M. Ding, M. Tomizuka, and Y. Wei, “Roadbev: Road surface reconstruction in bird’s eye view,”
2024
Later among the works it cites.
L. Yang, X. Zhang, J. Yu, J. Li, T. Zhao, L. Wang, Y. Huang, C. Zhang, H. Wang, and Y. Li, “Monogae: Roadside monocular 3d object detection with ground-aware embeddings,”
2024
Later among the works it cites.
T. Wang, F. Lu, Z. Zheng, Z. Li, G. Chen,
2024
Later among the works it cites.
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,”
2024
Later among the works it cites.
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
2024
Later among the works it cites.
C. Lin, D. Tian, X. Duan, J. Zhou, D. Zhao, and D. Cao, “V2vformer: Vehicle-to-vehicle cooperative perception with spatial-channel transformer,”
2024
Later among the works it cites.
Z. Song, T. Xie, H. Zhang, J. Liu, F. Wen, and J. Li, “A spatial calibration method for robust cooperative perception,”
2024
Later among the works it cites.
J. Zhang, K. Yang, Y. Wang, H. Wang, P. Sun, and L. Song, “Ermvp: Communication-efficient and collaboration-robust multi-vehicle perception in challenging environments,” in
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Later among the works it cites.
R. Xu, C.-J. Chen, Z. Tu, and M.-H. Yang, “V2x-vitv2: Improved vision transformers for vehicle-to-everything cooperative perception,”
2024
Later among the works it cites.
Z. Song, L. Liu, F. Jia, Y. Luo, C. Jia, G. Zhang, L. Yang, and L. Wang, “Robustness-aware 3d object detection in autonomous driving: A review and outlook,”
2024
Later among the works it cites.
X. Li, J. Yin, W. Li, C. Xu, R. Yang, and J. Shen, “Di-v2x: Learning domain-invariant representation for vehicle-infrastructure collaborative 3d object detection,” in
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Gong, X. Jiang, L. Wang, L. Xu, J. Lu, H. Liu, L. Lin, and X. Zhang, “Tclanenet: Task-conditioned lane detection network driven by vibration information,”
2024
Later among the works it cites.
Y. Gong, X. Zhang, J. Lu, X. Jiang, Z. Wang, H. Liu, Z. Li, L. Wang, Q. Yang, and X. Wu, “Steering angle-guided multimodal fusion lane detection for autonomous driving,”
2024
Later among the works it cites.
C. Xia, X. Wang, F. Lv, X. Hao, and Y. Shi, “Vit-comer: Vision transformer with convolutional multi-scale feature interaction for dense predictions,” in
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Later among the works it cites.
A. Abdulmaksoud and R. Ahmed, “Transformer-based sensor fusion for autonomous vehicles: A comprehensive review,”
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