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Motion prediction is among the most fundamental tasks in autonomous driving.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
T. Zhao, Y. Xu, M. Monfort, W. Choi, C. Baker, Y. Zhao, Y. Wang, and Y. N. Wu, “Multi-agent tensor fusion for contextual trajectory prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 126–12 134
2019
Earlier work this paper cites.
Y. Xu, X. Yang, L. Gong, H.-C. Lin, T.-Y. Wu, Y. Li, and N. Vasconcelos, “Explainable object-induced action decision for autonomous vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9523–9532
2020
Earlier work this paper cites.
J. Gu, C. Sun, and H. Zhao, “Densetnt: End-to-end trajectory prediction from dense goal sets,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 303–15 312
2021
Earlier work this paper cites.
H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, C. Schmid, et al. , “Tnt: Target-driven trajectory prediction,” in Conference on Robot Learning . PMLR, 2021, pp. 895–904
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
K. Chitta, A. Prakash, and A. Geiger, “Neat: Neural attention fields for end-to-end autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 793–15 803
2021
Earlier work this paper cites.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, et al. , “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9710–9719
2021
Earlier work this paper cites.
S. Shi, L. Jiang, D. Dai, and B. Schiele, “Motion transformer with global intention localization and local movement refinement,” Advances in Neural Information Processing Systems , vol. 35, pp. 6531–6543, 2022
2022
Earlier work this paper cites.
B. Varadarajan, A. Hefny, A. Srivastava, K. S. Refaat, N. Nayakanti, A. Cornman, K. Chen, B. Douillard, C. P. Lam, D. Anguelov, et al. , “Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 7814–7821
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
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2022
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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S. Shi, L. Jiang, D. Dai, and S. Bernt, “Mtr++: Multi-agent motion prediction with symmetric scene modeling and guided intention querying,” 2024
2024
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