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This paper addresses motion forecasting in multi-agent environments, pivotal for ensuring safety of autonomous vehicles.
1907
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V. Kosaraju, A. Sadeghian, R. Martín-Martín, I. D. Reid, H. Rezatofighi, and S. Savarese, “Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019, pp. 137–146. [Online]. Available: https://proceedings.neurips.cc/paper/2019/hash/d09bf41544a3365a46c9077ebb5e35c3-Abstract.html
2019
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Y. C. Tang and R. Salakhutdinov, “Multiple futures prediction,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019, pp. 15 398–15 408. [Online]. Available: https://proceedings.neurips.cc/paper/2019/hash/86a1fa88adb5c33bd7a68ac2f9f3f96b-Abstract.html
2019
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M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3d tracking and forecasting with rich maps,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 . Computer Vision Foundation / IEEE, 2019, pp. 8748–8757. [Online]. Available: http://openaccess.thecvf.com/content_CVPR_2019/html/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.html
2019
Cited alongside, same era.
M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II , ser. Lecture Notes in Computer Science, A. Vedaldi, H. Bischof, T. Brox, and J. Frahm, Eds., vol. 12347. Springer, 2020, pp. 541–556. [Online]. Available: https://doi.org/10.1007/978-3-030-58536-5_32
2020
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Q. Sun, X. Huang, J. Gu, B. C. Williams, and H. Zhao, “M2I: from factored marginal trajectory prediction to interactive prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022 . IEEE, 2022, pp. 6533–6542. [Online]. Available: https://doi.org/10.1109/CVPR52688.2022.00643
2022
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J. Ngiam, V. Vasudevan, B. Caine, Z. Zhang, H. L. Chiang, J. Ling, R. Roelofs, A. Bewley, C. Liu, A. Venugopal, D. J. Weiss, B. Sapp, Z. Chen, and J. Shlens, “Scene transformer: A unified architecture for predicting future trajectories of multiple agents,” in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net, 2022. [Online]. Available: https://openreview.net/forum?id=Wm3EA5OlHsG
2022
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F. Sun, I. Kauvar, R. Zhang, J. Li, M. J. Kochenderfer, J. Wu, and N. Haber, “Interaction modeling with multiplex attention,” in NeurIPS , 2022. [Online]. Available: http://papers.nips.cc/paper_files/paper/2022/hash/7e6361a5d73a8fab093dd8453e0b106f-Abstract-Conference.html
2022
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A. A. Mohamed, K. Qian, M. Elhoseiny, and C. G. Claudel, “Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . Computer Vision Foundation / IEEE, 2020, pp. 14 412–14 420. [Online]. Available: https://openaccess.thecvf.com/content_CVPR_2020/html/Mohamed_Social-STGCNN_A_Social_Spatio-Temporal_Graph_Convolutional_Neural_Network_for_Human_CVPR_2020_paper.html
2020
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S. Casas, C. Gulino, R. Liao, and R. Urtasun, “Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 9491–9497. [Online]. Available: https://doi.org/10.1109/ICRA40945.2020.9196697
2020
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S. Casas, C. Gulino, S. Suo, K. Luo, R. Liao, and R. Urtasun, “Implicit latent variable model for scene-consistent motion forecasting,” in Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIII , ser. Lecture Notes in Computer Science, A. Vedaldi, H. Bischof, T. Brox, and J. Frahm, Eds., vol. 12368. Springer, 2020, pp. 624–641. [Online]. Available: https://doi.org/10.1007/978-3-030-58592-1_37
2020
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T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVIII , ser. Lecture Notes in Computer Science, A. Vedaldi, H. Bischof, T. Brox, and J. Frahm, Eds., vol. 12363. Springer, 2020, pp. 683–700. [Online]. Available: https://doi.org/10.1007/978-3-030-58523-5_40
2020
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S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, Z. Yang, A. Chouard, P. Sun, J. Ngiam, V. Vasudevan, A. McCauley, J. Shlens, and D. Anguelov, “Large scale interactive motion forecasting for autonomous driving : The waymo open motion dataset,” in 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021 . IEEE, 2021, pp. 9690–9699. [Online]. Available: https://doi.org/10.1109/ICCV48922.2021.00957
2021
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A. K. Lampinen, N. A. Roy, I. Dasgupta, S. C. Y. Chan, A. C. Tam, J. L. McClelland, C. Yan, A. Santoro, N. C. Rabinowitz, J. X. Wang, and F. Hill, “Tell me why! explanations support learning relational and causal structure,” in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, and S. Sabato, Eds., vol. 162. PMLR, 2022, pp. 11 868–11 890. [Online]. Available: https://proceedings.mlr.press/v162/lampinen22a.html
2022
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Y. Kuo, X. Huang, A. Barbu, S. G. McGill, B. Katz, J. J. Leonard, and G. Rosman, “Trajectory prediction with linguistic representations,” in 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022 . IEEE, 2022, pp. 2868–2875. [Online]. Available: https://doi.org/10.1109/ICRA46639.2022.9811928
2022
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L. Sun, C. Tang, Y. Niu, E. Sachdeva, C. Choi, T. Misu, M. Tomizuka, and W. Zhan, “Domain knowledge driven pseudo labels for interpretable goal-conditioned interactive trajectory prediction,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022 . IEEE, 2022, pp. 13 034–13 041. [Online]. Available: https://doi.org/10.1109/IROS47612.2022.9982147
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