Fetching the paper…
Reading the bibliography…
Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception.
Welch, G., Bishop, G., et al.: An introduction to the kalman filter (1995)
1995
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9
1997
Earlier work this paper cites.
Jiang, D., Delgrossi, L.: Ieee 802.11p: Towards an international standard for wireless access in vehicular environments. VTC Spring 2008 - IEEE Vehicular Technology Conference pp. 2036–2040 (2008)
2008
Earlier work this paper cites.
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th annual international conference on machine learning. pp. 41–48 (2009)
2009
Earlier work this paper cites.
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of sumo-simulation of urban mobility. International journal on advances in systems and measurements 5
2012
Earlier work this paper cites.
Araniti, G., Campolo, C., Condoluci, M., Iera, A., Molinaro, A.: Lte for vehicular networking: a survey. IEEE Communications Magazine 51
2013
Earlier work this paper cites.
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on robot learning. pp. 1–16. PMLR (2017)
2017
Earlier work this paper cites.
Lee, K., Kim, J., Park, Y., Wang, H., Hong, D.: Latency of cellular-based v2x: Perspectives on tti-proportional latency and tti-independent latency. IEEE Access 5
2017
Earlier work this paper cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017)
2017
Earlier work this paper cites.
Wang, Y., Long, M., Wang, J., Gao, Z., Yu, P.S.: Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Mei, J., Zheng, K., Zhao, L., Teng, Y., Wang, X.: A latency and reliability guaranteed resource allocation scheme for lte v2v communication systems. IEEE Transactions on Wireless Communications 17
2018
Earlier work this paper cites.
Oliu, M., Selva, J., Escalera, S.: Folded recurrent neural networks for future video prediction. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 716–731 (2018)
2018
Earlier work this paper cites.
Wang, Y., Gao, Z., Long, M., Wang, J., Philip, S.Y.: Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In: International Conference on Machine Learning. pp. 5123–5132. PMLR (2018)
2018
Cited alongside, same era.
Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3075–3084 (2019)
2019
Cited alongside, same era.
Shi, S., Wang, X., Li, H.: Pointrcnn: 3d object proposal generation and detection from point cloud. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 770–779 (2019)
2019
Cited alongside, same era.
Wang, Y., Jiang, L., Yang, M.H., Li, L.J., Long, M., Fei-Fei, L.: Eidetic 3d lstm: A model for video prediction and beyond. In: ICLR (2019)
2019
Cited alongside, same era.
Li, Y., Ren, S., Wu, P., Chen, S., Feng, C., Zhang, W.: Learning distilled collaboration graph for multi-agent perception. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Vadivelu, N., Ren, M., Tu, J., Wang, J., Urtasun, R.: Learning to communicate and correct pose errors. In: Conference on Robot Learning. pp. 1195–1210. PMLR (2021)
2021
Later among the works it cites.
Yin, T., Zhou, X., Krahenbuhl, P.: Center-based 3d object detection and tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11784–11793 (2021)
2021
Later among the works it cites.
Yuan, Y., Sester, M.: Comap: A synthetic dataset for collective multi-agent perception of autonomous driving. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 43
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., Yu, P.S.: Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9154–9162 (2019)
2019
Cited alongside, same era.
Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., Wellington, C.: 3d point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception. IEEE Signal Processing Magazine 38
2020
Cited alongside, same era.
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43
2020
Cited alongside, same era.
Liu, Y.C., Tian, J., Glaser, N., Kira, Z.: When2com: Multi-agent perception via communication graph grouping. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. pp. 4106–4115 (2020)
2020
Cited alongside, same era.
Liu, Y.C., Tian, J., Ma, C.Y., Glaser, N., Kuo, C.W., Kira, Z.: Who2com: Collaborative perception via learnable handshake communication. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 6876–6883. IEEE (2020)
2020
Cited alongside, same era.
Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., Li, H.: Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10529–10538 (2020)
2020
Cited alongside, same era.
Su, J., Byeon, W., Kossaifi, J., Huang, F., Kautz, J., Anandkumar, A.: Convolutional tensor-train lstm for spatio-temporal learning. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Wang, T.H., Manivasagam, S., Liang, M., Yang, B., Zeng, W., Urtasun, R.: V2vnet: Vehicle-to-vehicle communication for joint perception and prediction. In: European Conference on Computer Vision. pp. 605–621. Springer (2020)
2020
Cited alongside, same era.
Zhang, X., Zhang, A., Sun, J., Zhu, X., Guo, Y.E., Qian, F., Mao, Z.M.: Emp: Edge-assisted multi-vehicle perception. In: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking. pp. 545–558 (2021)
2021
Later among the works it cites.
Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 16259–16268 (2021)
2021
Later among the works it cites.
2022
Closest in time.
Cui, J., Qiu, H., Chen, D., Stone, P., Zhu, Y.: Coopernaut: End-to-end driving with cooperative perception for networked vehicles. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17252–17262 (2022)
2022
Closest in time.
Li, Y., An, Z., Wang, Z., Zhong, Y., Chen, S., Feng, C.: V2X-Sim: A virtual collaborative perception dataset for autonomous driving. IEEE Robotics and Automation Letters (2022)
2022
Closest in time.
2022
Closest in time.
Xu, R., Xiang, H., Xia, X., Han, X., Li, J., Ma, J.: Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In: 2022 International Conference on Robotics and Automation (ICRA). pp. 2583–2589. IEEE (2022)
2022
Closest in time.
Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., Yuan, J., 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. pp. 21361–21370 (2022)
2022
Closest in time.
Yuan, Y., Cheng, H., Sester, M.: Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving. IEEE Robotics and Automation Letters 7
2022
Closest in time.