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Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving.
2017
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2019
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2020
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J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding hd maps and agent dynamics from vectorized representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 525–11 533
2020
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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: 16th European Conference, Glasgow, Proceedings, Part II 16 . Springer, 2020
2020
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2020
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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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
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T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff, “Covernet: Multimodal behavior prediction using trajectory sets,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 14 074–14 083
2020
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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: 16th European Conference, Glasgow, Proceedings, Part XVIII 16 . Springer, 2020
2020
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S. Kumar, Y. Gu, J. Hoang, G. C. Haynes, and M. Marchetti-Bowick, “Interaction-based trajectory prediction over a hybrid traffic graph,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5530–5535
M. Zipfl, F. Hertlein, A. Rettinger, S. Thoma, L. Halilaj, J. Luettin, S. Schmid, and C. Henson, “Relation-based motion prediction using traffic scene graphs,” in 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022
2022
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Z. Huang, X. Mo, and C. Lv, “Multi-modal motion prediction with transformer-based neural network for autonomous driving,” in Int. Conference on Robotics and Automation (ICRA) . IEEE, 2022
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Z. Sheng, Y. Xu, S. Xue, and D. Li, “Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 10, pp. 17 654–17 665, 2022
2022
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M. Zipfl and J. M. Zöllner, “Towards traffic scene description: The semantic scene graph,” in 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 3748–3755
2022
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2021
Cited alongside, same era.
E. M. Rella, J.-N. Zaech, A. Liniger, and L. Van Gool, “Decoder fusion rnn: Context and interaction aware decoders for trajectory prediction,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5937–5943
2021
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Y. Yuan, X. Weng, Y. Ou, and K. M. Kitani, “Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9813–9823
2021
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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
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H. Berkemeyer, R. Franceschini, T. Tran, L. Che, and G. Pipa, “Feasible and adaptive multimodal trajectory prediction with semantic maneuver fusion,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2021
2021
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L. Halilaj, I. Dindorkar, J. Lüttin, and S. Rothermel, “A knowledge graph-based approach for situation comprehension in driving scenarios,” in The Semantic Web: 18th International Conference, ESWC Proceedings . Springer, 2021
2021
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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
2021
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D. Cao, J. Li, H. Ma, and M. Tomizuka, “Spectral temporal graph neural network for trajectory prediction,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2021
2021
Cited alongside, same era.
Y. Huang, J. Du, Z. Yang, Z. Zhou, L. Zhang, and H. Chen, “A survey on trajectory-prediction methods for autonomous driving,” IEEE Transactions on Intelligent Vehicles , vol. 7, pp. 652–674, 2022
2022
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Later among the works it cites.
R. Girgis, F. Golemo, F. Codevilla, M. Weiss, J. A. D’Souza, S. E. Kahou, F. Heide, and C. Pal, “Latent variable sequential set transformers for joint multi-agent motion prediction,” in International Conference on Learning Representations, ICLR 2022 , 2022
2022
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C. Wang, Y. Wang, M. Xu, and D. J. Crandall, “Stepwise goal-driven networks for trajectory prediction,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 2716–2723, 2022
2022
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Z. Ding and H. Zhao, “Incorporating driving knowledge in deep learning based vehicle trajectory prediction: A survey,” IEEE Transactions on Intelligent Vehicles , vol. 8, pp. 3996–4015, 2023
2023
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X. Jia, P. Wu, L. Chen, Y. Liu, H. Li, and J. Yan, “Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding,” IEEE transactions on pattern analysis and machine intelligence , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Park, H. Ryu, Y. Yang, J. Cho, J. Kim, and K. Yoon, “Leveraging future relationship reasoning for vehicle trajectory prediction,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2023
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D. Grimm, P. Schörner, M. Dreßler, and J.-M. Zöllner, “Holistic graph-based motion prediction,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2023
2023
Later among the works it cites.
D. Grimm, M. Zipfl, F. Hertlein, A. Naumann, J. Lüttin, S. Thoma, S. Schmid, L. Halilaj, A. Rettinger, and J. M. Zöllner, “Heterogeneous graph-based trajectory prediction using local map context and social interactions,” 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) , pp. 2901–2907, 2023
2023
Later among the works it cites.
L. Mlodzian, Z. Sun, H. Berkemeyer, S. Monka, Z. Wang, S. Dietze, L. Halilaj, and J. Luettin, “nuscenes knowledge graph-a comprehensive semantic representation of traffic scenes for trajectory prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 42–52
2023
Later among the works it cites.
H. Jeon, J. Choi, and D. Kum, “Scale-net: Scalable vehicle trajectory prediction network under random number of interacting vehicles via edge-enhanced graph convolutional neural network,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2095–2102
2095
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