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Trajectory prediction is a pivotal component of autonomous driving systems, enabling the application of accumulated movement experience to current scenarios.
I. Hajime, B. ATSUMI, U. Hiroshi, and M. AKAMATSU, “Visual distraction while driving: trends in research and standardization,” IATSS research , vol. 25, no. 2, pp. 20–28, 2001
2001
Earlier work this paper cites.
X. Wu, G. Liang, K. K. Lee, and Y. Xu, “Crowd density estimation using texture analysis and learning,” in 2006 IEEE international conference on robotics and biomimetics . IEEE, 2006, pp. 214–219
2006
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes, “Memory networks,” arXiv preprint arXiv:1410.3916 , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 961–971
2016
Earlier work this paper cites.
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.
A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Louie, “Working memory capacity and executive attention as predictors of distracted driving,” 2018
2018
Earlier work this paper cites.
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2255–2264
2018
Earlier work this paper cites.
N. Deo and M. M. Trivedi, “Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms,” in 2018 IEEE intelligent vehicles symposium (IV) . IEEE, 2018, pp. 1179–1184
2018
Earlier work this paper cites.
Y. Xing, C. Lv, and D. Cao, “Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles,” IEEE Transactions on Vehicular Technology , vol. 69, no. 2, pp. 1341–1352, 2019
2019
Earlier work this paper cites.
Y. Huang, H. Bi, Z. Li, T. Mao, and Z. Wang, “Stgat: Modeling spatial-temporal interactions for human trajectory prediction,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 6272–6281
2019
Earlier work this paper cites.
V. Kosaraju, A. Sadeghian, R. Martín-Martín, I. Reid, H. Rezatofighi, and S. Savarese, “Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
P. Zhang, W. Ouyang, P. Zhang, J. Xue, and N. Zheng, “Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 085–12 094
2019
Earlier work this paper cites.
F. Marchetti, F. Becattini, L. Seidenari, and A. D. Bimbo, “Mantra: Memory augmented networks for multiple trajectory prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 7143–7152
2020
Earlier work this paper cites.
A. Mohamed, K. Qian, M. Elhoseiny, and C. Claudel, “Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 14 424–14 432
2020
Earlier work this paper cites.
C. Yu, X. Ma, J. Ren, H. Zhao, and S. Yi, “Spatio-temporal graph transformer networks for pedestrian trajectory prediction,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16 . Springer, 2020, pp. 507–523
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
K. Mangalam, H. Girase, S. Agarwal, K.-H. Lee, E. Adeli, J. Malik, and A. Gaidon, “It is not the journey but the destination: Endpoint conditioned trajectory prediction,” in European Conference on Computer Vision . Springer, 2020, pp. 759–776
2020
Cited alongside, same era.
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 16 . Springer, 2020, pp. 683–700
2020
Cited alongside, same era.
L. Xin, Y. Xiaowen, and C. C. Mooi, “Grip++: Enhanced graph-based interaction-aware trajectory prediction for autonomous driving,” in arXiv preprint arXiv: 1907.07792 , 2020
2020
Y. Zhang, W. Wang, W. Guo, P. Lv, M. Xu, W. Chen, and D. Manocha, “D2-tpred: Discontinuous dependency for trajectory prediction under traffic lights,” in European Conference on Computer Vision . Springer, 2022, pp. 522–539
2022
Later among the works it cites.
B. Li, Y. Fang, S. Ma, H. Wang, Y. Wang, X. Li, T. Zhang, X. Bian, F.-Y. Wang et al. , “Toward fair and thrilling autonomous racing: Governance rules and performance metrics for autonomous one,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Later among the works it cites.
X. He, H. Chen, and C. Lv, “Robust multiagent reinforcement learning toward coordinated decision-making of automated vehicles,” SAE International Journal of Vehicle Dynamics, Stability, and NVH , 2023
2023
Later among the works it cites.
2023
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Cited alongside, same era.
H. Xue, D. Q. Huynh, and M. Reynolds, “Poppl: Pedestrian trajectory prediction by lstm with automatic route class clustering,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 77–90, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
B. Yang, G. Yan, P. Wang, C.-Y. Chan, X. Song, and Y. Chen, “A novel graph-based trajectory predictor with pseudo-oracle,” IEEE transactions on neural networks and learning systems , 2021
2021
Cited alongside, same era.
L. Shi, L. Wang, C. Long, S. Zhou, M. Zhou, Z. Niu, and G. Hua, “Sgcn: Sparse graph convolution network for pedestrian trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8994–9003
2021
Cited alongside, same era.
N. Shafiee, T. Padir, and E. Elhamifar, “Introvert: Human trajectory prediction via conditional 3d attention,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 16 815–16 825
2021
Cited alongside, same era.
K. Mangalam, Y. An, H. Girase, and J. Malik, “From goals, waypoints & paths to long term human trajectory forecasting,” in Proc. International Conference on Computer Vision (ICCV) , Oct. 2021
2021
Cited alongside, same era.
H. Cheng, W. Liao, M. Y. Yang, B. Rosenhahn, and M. Sester, “Amenet: Attentive maps encoder network for trajectory prediction,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 172, pp. 253–266, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S092427162030335X
2021
Cited alongside, same era.
S. Carrasco, D. F. Llorca, and M. A. Sotelo, “Scout: Socially-consistent and understandable graph attention network for trajectory prediction of vehicles and vrus,” in 2021 IEEE Intelligent Vehicles Symposium (IV) , 2021, pp. 1501–1508
2021
Cited alongside, same era.
Later among the works it cites.
L. Shi, L. Wang, S. Zhou, and G. Hua, “Trajectory unified transformer for pedestrian trajectory prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9675–9684
2023
Later among the works it cites.
A. Seff, B. Cera, D. Chen, M. Ng, A. Zhou, N. Nayakanti, K. S. Refaat, R. Al-Rfou, and B. Sapp, “Motionlm: Multi-agent motion forecasting as language modeling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8579–8590
2023
Later among the works it cites.
X. Mo, H. Liu, Z. Huang, X. Li, and C. Lv, “Map-adaptive multimodal trajectory prediction via intention-aware unimodal trajectory predictors,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
X. He, J. Wu, Z. Huang, Z. Hu, J. Wang, A. Sangiovanni-Vincentelli, and C. Lv, “Fear-neuro-inspired reinforcement learning for safe autonomous driving,” IEEE transactions on pattern analysis and machine intelligence , 2023
2023
Later among the works it cites.
X. Chen, H. Zhang, F. Deng, J. Liang, and J. Yang, “Stochastic non-autoregressive transformer-based multi-modal pedestrian trajectory prediction for intelligent vehicles,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
A. Aksjonov and V. Kyrki, “A safety-critical decision-making and control framework combining machine-learning-based and rule-based algorithms,” SAE International Journal of Vehicle Dynamics, Stability, and NVH , vol. 7, no. 10-07-03-0018, pp. 287–299, 2023
2023
Later among the works it cites.
T. Salzmann, H.-T. L. Chiang, M. Ryll, D. Sadigh, C. Parada, and A. Bewley, “Robots that can see: Leveraging human pose for trajectory prediction,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Zhang, W. Guo, J. Su, P. Lv, and M. Xu, “Bip-tree: Tree variant with behavioral intention perception for heterogeneous trajectory prediction,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
S. Chen and J. Wang, “Heterogeneous interaction modeling with reduced accumulated error for multiagent trajectory prediction,” IEEE Transactions on Neural Networks and Learning Systems , vol. 35, no. 6, pp. 8040–8052, 2024
2024
Closest in time.
X. Tang, M. Kan, S. Shan, Z. Ji, J. Bai, and X. Chen, “Hpnet: Dynamic trajectory forecasting with historical prediction attention,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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Y. Zhou, Z. Wang, N. Ning, Z. Jin, N. Lu, and X. Shen, “I2t: From intention decoupling to vehicular trajectory prediction based on prioriformer networks,” IEEE Transactions on Intelligent Transportation Systems , 2024
2024
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C. Xu, Y. Wei, B. Tang, S. Yin, Y. Zhang, S. Chen, and Y. Wang, “Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning,” Neural Networks , vol. 170, pp. 564–577, 2024
2024
Closest in time.
C. Wong, B. Xia, Z. Zou, Y. Wang, and X. You, “Socialcircle: Learning the angle-based social interaction representation for pedestrian trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 005–19 015
2024
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A. Feng, C. Han, J. Gong, Y. Yi, R. Qiu, and Y. Cheng, “Multi-scale learnable gabor transform for pedestrian trajectory prediction from different perspectives,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–11, 2024
2024
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