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Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways.
1911
Earlier work this paper cites.
D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Phys Rev E, vol. 51, no. 5, 1995, doi: 10.1103/PhysRevE.51.4282
1995
Earlier work this paper cites.
C. F. Lin, A. G. Ulsoy, and D. J. LeBlanc, “Vehicle dynamics and external disturbance estimation for vehicle path prediction,” IEEE Transactions on Control Systems Technology, vol. 8, no. 3, 2000, doi: 10.1109/87.845881
2000
Earlier work this paper cites.
J. Colyar and J. Halkias, “U.S. Highway 80 dataset,” 2006
2006
Earlier work this paper cites.
J. Colyar and J. Halkias, “U.S. Highway 101 dataset,” 2007
2007
Earlier work this paper cites.
2008
Earlier work this paper cites.
S. Ammoun and F. Nashashibi, “Real time trajectory prediction for collision risk estimation between vehicles,” in Proceedings - 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, ICCP 2009, 2009. doi: 10.1109/ICCP.2009.5284727
2009
Earlier work this paper cites.
C. Laugier et al., “Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety,” IEEE Intelligent Transportation Systems Magazine, vol. 3, no. 4, 2011, doi: 10.1109/MITS.2011.942779
2011
Earlier work this paper cites.
A. Houenou, P. Bonnifait, V. Cherfaoui, and W. Yao, “Vehicle trajectory prediction based on motion model and maneuver recognition,” in IEEE International Conference on Intelligent Robots and Systems, 2013. doi: 10.1109/IROS.2013.6696982
2013
Earlier work this paper cites.
Q. Tran and J. Firl, “Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2014. doi: 10.1109/IVS.2014.6856480
2014
Earlier work this paper cites.
T. Gindele, S. Brechtel, and R. Dillmann, “Learning driver behavior models from traffic observations for decision making and planning,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 1, 2015, doi: 10.1109/MITS.2014.2357038
2014
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 Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-December. doi: 10.1109/CVPR.2016.110
2016
Earlier work this paper cites.
Y. Rasekhipour, A. Khajepour, S. K. Chen, and B. Litkouhi, “A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, 2017, doi: 10.1109/TITS.2016.2604240
2016
Earlier work this paper cites.
D. J. Phillips, T. A. Wheeler, and M. J. Kochenderfer, “Generalizable intention prediction of human drivers at intersections,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2017. doi: 10.1109/IVS.2017.7995948
2017
Earlier work this paper cites.
B. Do Kim, C. M. Kang, J. Kim, S. H. Lee, C. C. Chung, and J. W. Choi, “Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018, vol. 2018-March. doi: 10.1109/ITSC.2017.8317943
2017
Earlier work this paper cites.
N. Deo and M. M. Trivedi, “Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2018, vol. 2018-June. doi: 10.1109/IVS.2018.8500493
2018
Earlier work this paper cites.
N. Deo and M. M. Trivedi, “Convolutional social pooling for vehicle trajectory prediction,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018, vol. 2018-June. doi: 10.1109/CVPRW.2018.00196
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 Computer Society Conference on Computer Vision and Pattern Recognition, 2018. doi: 10.1109/CVPR.2018.00240
2018
Earlier work this paper cites.
R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018, vol. 2018-November. doi: 10.1109/ITSC.2018.8569552
2018
Earlier work this paper cites.
Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, 2018
2018
Earlier work this paper cites.
S. H. Park, B. Kim, C. M. Kang, C. C. Chung, and J. W. Choi, “Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2018. doi: 10.1109/IVS.2018.8500658
2018
Earlier work this paper cites.
D. Park, H. Ryu, Y. Yang, J. Cho, J. Kim, and K.-J. Yoon, ”Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction,” in The Eleventh International Conference on Learning Representations, 2023. [Online]. Available: https://openreview.net/forum?id=CGBCTp2M6lA
2018
Cited alongside, same era.
J. Liu, Y. Luo, H. Xiong, T. Wang, H. Huang, and Z. Zhong, “An Integrated Approach to Probabilistic Vehicle Trajectory Prediction via Driver Characteristic and Intention Estimation,” in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2019. doi: 10.1109/ITSC.2019.8917039
2019
Cited alongside, same era.
G. He, X. Li, Y. Lv, B. Gao, and H. Chen, “Probabilistic intention prediction and trajectory generation based on dynamic bayesian networks,” in Proceedings - 2019 Chinese Automation Congress, CAC 2019, 2019. doi: 10.1109/CAC48633.2019.8996494
2019
Cited alongside, same era.
H. Cui et al., “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in Proceedings - IEEE International Conference on Robotics and Automation, 2019, vol. 2019-May. doi: 10.1109/ICRA.2019.8793868
H. Song, W. Ding, Y. Chen, S. Shen, M. Y. Wang, and Q. Chen, “PiP: Planning-Informed Trajectory Prediction for Autonomous Driving,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, vol. 12366 LNCS. doi: 10.1007/978-3-030-58589-1_36
2020
Later among the works it cites.
S. Casas, C. Gulino, S. Suo, K. Luo, R. Liao, and R. Urtasun, “Implicit Latent Variable Model for Scene-Consistent Motion Forecasting,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020. doi: 10.1007/978-3-030-58592-1_37
2020
Later among the works it cites.
H. Zhou, D. Ren, H. Xia, M. Fan, X. Yang, and H. Huang, “AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction,” Neurocomputing, vol. 445, 2021, doi: 10.1016/j.neucom.2021.03.024
2021
Later among the works it cites.
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2019
Cited alongside, same era.
X. Li, X. Ying, and M. C. Chuah, “GRIP: Graph-based Interaction-aware Trajectory Prediction,” in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2019. doi: 10.1109/ITSC.2019.8917228
2019
Cited alongside, same era.
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 International Conference on Computer Vision, 2019, vol. 2019-October. doi: 10.1109/ICCV.2019.00637
2019
Cited alongside, same era.
K. Messaoud, I. Yahiaoui, A. Verroust-Blondet, and F. Nashashibi, “Non-local social pooling for vehicle trajectory prediction,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2019, vol. 2019-June. doi: 10.1109/IVS.2019.8813829
2019
Cited alongside, same era.
T. Zhao et al., “Multi-agent tensor fusion for contextual trajectory prediction,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019, vol. 2019-June. doi: 10.1109/CVPR.2019.01240
2019
Cited alongside, same era.
J. Amirian, J. B. Hayet, and J. Pettre, “Social ways: Learning multi-modal distributions of pedestrian trajectories with GANs,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019, vol. 2019-June. doi: 10.1109/CVPRW.2019.00359
2019
Cited alongside, same era.
X. Feng, Z. Cen, J. Hu, and Y. Zhang, “Vehicle Trajectory Prediction Using Intention-based Conditional Variational Autoencoder,” in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2019. doi: 10.1109/ITSC.2019.8917482
2019
Cited alongside, same era.
A. Sadeghian, V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese, “SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019. doi: 10.1109/CVPR.2019.00144
2019
Cited alongside, same era.
J. Li, H. Ma, and M. Tomizuka, “Conditional Generative Neural System for Probabilistic Trajectory Prediction,” in IEEE International Conference on Intelligent Robots and Systems, 2019. doi: 10.1109/IROS40897.2019.8967822
2019
Cited alongside, same era.
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, Proceedings, 2021, vol. 2021-July. doi: 10.1109/IV48863.2021.9576054
2021
Later among the works it cites.
L. Shi et al., “SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021. doi: 10.1109/CVPR46437.2021.00888
2021
Later among the works it cites.
S. Malla, C. Choi, and B. Dariush, “Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast,” in Proceedings - IEEE International Conference on Robotics and Automation, 2021, vol. 2021-May. doi: 10.1109/ICRA48506.2021.9561582
2021
Later among the works it cites.
J. Gu, C. Sun, and H. Zhao, “DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets,” in Proceedings of the IEEE International Conference on Computer Vision, 2021. doi: 10.1109/ICCV48922.2021.01502
2021
Later among the works it cites.
W. Zeng, M. Liang, R. Liao, and R. Urtasun, “LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting,” in IEEE International Conference on Intelligent Robots and Systems, 2021. doi: 10.1109/IROS51168.2021.9636035
2021
Later among the works it cites.
K. Shi, Y. Wu, H. Shi, Y. Zhou, and B. Ran, “An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network,” Physica A: Statistical Mechanics and its Applications, vol. 599, 2022, doi: 10.1016/j.physa.2022.127303
2022
Later among the works it cites.
P. Karle, M. Geisslinger, J. Betz, and M. Lienkamp, “Scenario Understanding and Motion Prediction for Autonomous Vehicles - Review and Comparison,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10. 2022. doi: 10.1109/TITS.2022.3156011
2022
Later among the works it cites.
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, no. 3, pp. 652–674, 2022, doi: 10.1109/TIV.2022.3167103
2022
Later among the works it cites.
X. Chen, H. Zhang, F. Zhao, Y. Hu, C. Tan, and J. Yang, “Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19471–19483, 2022, doi: 10.1109/TITS.2022.3170551
2022
Later among the works it cites.
K. Zhang, L. Zhao, C. Dong, L. Wu, and L. Zheng, “AI-TP: Attention-based Interaction-aware Trajectory Prediction for Autonomous Driving,” IEEE Transactions on Intelligent Vehicles, 2022, doi: 10.1109/TIV.2022.3155236
2022
Later among the works it cites.
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, 2022, doi: 10.1109/TITS.2022.3155749
2022
Later among the works it cites.
N. P. Bhatt, A. Khajepour, and E. Hashemi, “MPC-PF: Social Interaction Aware Trajectory Prediction of Dynamic Objects for Autonomous Driving Using Potential Fields,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 9837–9844. doi: 10.1109/IROS47612.2022.9981046
2022
Later among the works it cites.
X. Zheng, H. Li, H. Liu, X. Chen, and T. Luo, “A modeling method of driving risk assessment based on vehicle trajectory prediction,” in 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI), 2022, pp. 1–4. doi: 10.1109/DTPI55838.2022.9998885
2022
Later among the works it cites.
H. Song, D. Luan, W. Ding, M. Y. Wang, and Q. Chen, “Learning to Predict Vehicle Trajectories with Model-based Planning,” in Proceedings of the 5th Conference on Robot Learning, A. Faust, D. Hsu, and G. Neumann, Eds., in Proceedings of Machine Learning Research, vol. 164. PMLR, Apr. 2022, pp. 1035–1045. [Online]. Available: https://proceedings.mlr.press/v164/song22a.html
2022
Later among the works it cites.
H. Shi, D. Chen, N. Zheng, X. Wang, Y. Zhou, and B. Ran, “A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon,” Transp Res Part C Emerg Technol, vol. 148, 2023, doi: 10.1016/j.trc.2023.104019
2023
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H. Shi, Y. Zhou, K. Wu, S. Chen, B. Ran, and Q. Nie, “Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles,” Knowl Based Syst, vol. 269, 2023, doi: 10.1016/j.knosys.2023.110485
2023
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V. Trentin, A. Artuñedo, J. Godoy, and J. Villagra, “Multi-Modal Interaction-Aware Motion Prediction At Unsignalized Intersections,” IEEE Transactions on Intelligent Vehicles, pp. 1–17, 2023, doi: 10.1109/TIV.2023.3254657
2023
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