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Predicting the future motion of surrounding road users is a crucial and challenging task for autonomous driving (AD) and various advanced driver-assistance systems (ADAS).
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2018
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2020
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T. Zhao, Y. Xu, M. Monfort, W. Choi, C. Baker, Y. Zhao, Y. Wang, and Y. N. Wu, “Multi-agent tensor fusion for contextual trajectoy prediction,” in IEEE Conf. on Computer Vision and Pattern Recognition , 2019
2019
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H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric, “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in IEEE Intl. Conf. on Robotics and Automation , 2019
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M. Bansal, A. Krizhevsky, and A. S. Ogale, “Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst,” in Robotics: Science and Systems XV , 2019
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2019
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B. S. J. Hong and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,” in IEEE Conf. on Computer Vision and Pattern Recognition , 2019
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2019
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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 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Computer Vision Foundation / IEEE, 2020, pp. 11 618–11 628
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2020
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C. Luo, L. Sun, D. Dabiri, and A. Yuille, “Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles,” IEEE International Conference on Intelligent Robots and Systems , pp. 2370–2376, 2020
2020
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K. Messaoud, N. Deo, M. M. Trivedi, and F. Nashashibi, “Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation,” in IEEE Intelligent Vehicles Symposium, IV 2021 . IEEE, 2021, pp. 165–170
2021
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2021
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2021
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2021
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2021
Later among the works it cites.
N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F. Chou, T. Lin, N. Singh, and J. Schneider, “Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving,” in IEEE Winter Conference on Applications of Computer Vision, WACV . IEEE, 2020, pp. 2084–2093
2093
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