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Motion prediction has been an essential component of autonomous driving systems since it handles highly uncertain and complex scenarios involving moving agents of different types.
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L. Fang, Q. Jiang, J. Shi, and B. Zhou, “Tpnet: Trajectory proposal network for motion prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6797–6806
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P. Wu, S. Chen, and D. N. Metaxas, “Motionnet: Joint perception and motion prediction for autonomous driving based on bird’s eye view maps,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 385–11 395
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Z. Huang, X. Mo, and C. Lv, “Recoat: A deep learning-based framework for multi-modal motion prediction in autonomous driving application,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 988–993
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K. Chen, R. Ge, H. Qiu, R. Ai-Rfou, C. R. Qi, X. Zhou, Z. Yang, S. Ettinger, P. Sun, Z. Leng, M. Mustafa, I. Bogun, W. Wang, M. Tan, and D. Anguelov, “Womd-lidar: Raw sensor dataset benchmark for motion forecasting,” 2023
2023
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B. Wilson, W. Qi, T. Agarwal, J. Lambert, J. Singh, S. Khandelwal, B. Pan, R. Kumar, A. Hartnett, J. K. Pontes, D. Ramanan, P. Carr, and J. Hays, “Argoverse 2: Next generation datasets for self-driving perception and forecasting,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021) , 2021
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J. Gu, C. Sun, and H. Zhao, “Densetnt: End-to-end trajectory prediction from dense goal sets,” 2021
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2021
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H. Fan, B. Xiong, K. Mangalam, Y. Li, Z. Yan, J. Malik, and C. Feichtenhofer, “Multiscale vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 6824–6835
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N. Djuric, H. Cui, Z. Su, S. Wu, H. Wang, F.-C. Chou, L. San Martin, S. Feng, R. Hu, Y. Xu et al. , “Multixnet: Multiclass multistage multimodal motion prediction,” in 2021 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2021, pp. 435–442
2021
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A. Laddha, S. Gautam, S. Palombo, S. Pandey, and C. Vallespi-Gonzalez, “Mvfusenet: Improving end-to-end object detection and motion forecasting through multi-view fusion of lidar data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2865–2874
2021
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N. Nayakanti, R. Al-Rfou, A. Zhou, K. Goel, K. S. Refaat, and B. Sapp, “Wayformer: Motion forecasting via simple & efficient attention networks,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2980–2987
2023
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D. Ye, Z. Zhou, W. Chen, Y. Xie, Y. Wang, P. Wang, and H. Foroosh, “Lidarmultinet: Towards a unified multi-task network for lidar perception,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 3, 2023, pp. 3231–3240
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L. Haochen, M. Xiaoyu, H. Zhiyu, and L. Chen, “Transformer with group-wise modal assignments for motion prediction,” 2023
2023
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Y. Ting, J. Lingxin, and L. Wei, “Dmp: Destination-driven motion prediction with prior fusion,” 2023
2023
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N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, N. Singh, and J. Schneider, “Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 2095–2104
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