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Data-driven simulation has become a favorable way to train and test autonomous driving algorithms.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2011, pp. 627–635
2011
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
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.
N. Lee, W. Choi, P. Vernaza, C. B. Choy, P. H. Torr, and M. Chandraker, “Desire: Distant future prediction in dynamic scenes with interacting agents,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 336–345
2017
Earlier work this paper cites.
D. Ha and J. Schmidhuber, “Recurrent world models facilitate policy evolution,” in Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
N. Rhinehart, K. M. Kitani, and P. Vernaza, “R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 772–788
2018
Earlier work this paper cites.
P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in 2018 21st international conference on intelligent transportation systems (ITSC) . IEEE, 2018, pp. 2575–2582
2018
Earlier work this paper cites.
E. Leurent, “An environment for autonomous driving decision-making,” https://github.com/eleurent/highway-env , 2018
2018
Earlier work this paper cites.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International conference on machine learning . PMLR, 2019, pp. 2555–2565
2019
Earlier work this paper cites.
M. Henaff, A. Canziani, and Y. LeCun, “Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2019
2019
Earlier work this paper cites.
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine, “Precog: Prediction conditioned on goals in visual multi-agent settings,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2821–2830
2019
Earlier work this paper cites.
C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
B. Ivanovic and M. Pavone, “The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2375–2384
2019
Earlier work this paper cites.
F. Behbahani, K. Shiarlis, X. Chen, V. Kurin, S. Kasewa, C. Stirbu, J. Gomes, S. Paul, F. A. Oliehoek, J. Messias, et al. , “Learning from demonstration in the wild,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 775–781
2019
Earlier work this paper cites.
Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” in Conference on Robot Learning , 2019
2019
Cited alongside, same era.
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
Cited alongside, same era.
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2020
2020
Cited alongside, same era.
H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, and C. Schmid, “Tnt: Target-driven trajectory prediction,” in Conference on Robot Learning , 2020
2020
Cited alongside, same era.
S. W. Kim, J. Philion, A. Torralba, and S. Fidler, “Drivegan: Towards a controllable high-quality neural simulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 5820–5829
2021
Later among the works it cites.
2021
Later among the works it cites.
L. Bergamini, Y. Ye, O. Scheel, L. Chen, C. Hu, L. Del Pero, B. Osiński, H. Grimmett, and P. Ondruska, “Simnet: Learning reactive self-driving simulations from real-world observations,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 5119–5125
2021
Later among the works it cites.
S. Suo, S. Regalado, S. Casas, and R. Urtasun, “Trafficsim: Learning to simulate realistic multi-agent behaviors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 400–10 409
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S. Casas, C. Gulino, S. Suo, K. Luo, R. Liao, and R. Urtasun, “Implicit latent variable model for scene-consistent motion forecasting,” in European Conference on Computer Vision . Springer, 2020, pp. 624–641
2020
Cited alongside, same era.
T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in European Conference on Computer Vision . Springer, 2020, pp. 683–700
2020
Cited alongside, same era.
A. Amini, I. Gilitschenski, J. Phillips, J. Moseyko, R. Banerjee, S. Karaman, and D. Rus, “Learning robust control policies for end-to-end autonomous driving from data-driven simulation,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1143–1150, 2020
2020
Cited alongside, same era.
M. Zhou, J. Luo, J. Villela, Y. Yang, D. Rusu, J. Miao, W. Zhang, M. Alban, I. Fadakar, and Z. Chen, “SMARTS: Scalable multi-agent reinforcement learning training school for autonomous driving,” in Conference on Robot Learning . PMLR, 2020
2020
Cited alongside, same era.
R. Xiong, Y. Yang, D. He, K. Zheng, S. Zheng, C. Xing, H. Zhang, Y. Lan, L. Wang, and T. Liu, “On layer normalization in the transformer architecture,” in International Conference on Machine Learning . PMLR, 2020, pp. 10 524–10 533
2020
Cited alongside, same era.
Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool, “End-to-end urban driving by imitating a reinforcement learning coach,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 222–15 232
2021
Cited alongside, same era.
D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba, “Mastering atari with discrete world models,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
J. Ngiam, V. Vasudevan, B. Caine, Z. Zhang, H.-T. L. Chiang, J. Ling, R. Roelofs, A. Bewley, C. Liu, A. Venugopal, et al. , “Scene transformer: A unified architecture for predicting future trajectories of multiple agents,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
2021
Later among the works it cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Later among the works it cites.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, et al. , “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9710–9719
2021
Later among the works it cites.
D. Wu and Y. Wu, “Air2 for interaction prediction,” in Workshop on Autonomous Driving, CVPR , 2021
2021
Later among the works it cites.
X. Mo, Z. Huang, and C. Lv, “Multi-modal interactive agent trajectory prediction using heterogeneous edge-enhanced graph attention network,” in Workshop on Autonomous Driving, CVPR , 2021
2021
Later among the works it cites.
B. Varadarajan, A. Hefny, A. Srivastava, K. S. Refaat, N. Nayakanti, A. Cornman, K. Chen, B. Douillard, C. P. Lam, D. Anguelov, et al. , “Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 7814–7821
2022
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 Proceedings of the International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
N. Deo, E. Wolff, and O. Beijbom, “Multimodal trajectory prediction conditioned on lane-graph traversals,” in Conference on Robot Learning . PMLR, 2022, pp. 203–212
2022
Later among the works it cites.
J. L. V. Espinoza, A. Liniger, W. Schwarting, D. Rus, and L. Van Gool, “Deep interactive motion prediction and planning: Playing games with motion prediction models,” in Learning for Dynamics and Control Conference . PMLR, 2022, pp. 1006–1019
2022
Later among the works it cites.
A. Amini, T.-H. Wang, I. Gilitschenski, W. Schwarting, Z. Liu, S. Han, S. Karaman, and D. Rus, “Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2419–2426
2022
Later among the works it cites.
O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska, “Urban driver: Learning to drive from real-world demonstrations using policy gradients,” in Conference on Robot Learning . PMLR, 2022, pp. 718–728
2022
Later among the works it cites.
M. Igl, D. Kim, A. Kuefler, P. Mougin, P. Shah, K. Shiarlis, D. Anguelov, M. Palatucci, B. White, and S. Whiteson, “Symphony: Learning realistic and diverse agents for autonomous driving simulation,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2022
2022
Later among the works it cites.
A. Kamenev, L. Wang, O. B. Bohan, I. Kulkarni, B. Kartal, A. Molchanov, S. Birchfield, D. Nistér, and N. Smolyanskiy, “Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8936–8942
2022
Later among the works it cites.