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In recent years, imitation-based driving planners have reported considerable success.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E , vol. 62, no. 2, p. 1805, 2000
2000
Earlier work this paper cites.
U. Muller, J. Ben, E. Cosatto, B. Flepp, and Y. Cun, “Off-road obstacle avoidance through end-to-end learning,” Advances in neural information processing systems , vol. 18, 2005
2005
Earlier work this paper cites.
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.
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.
N. Baram, O. Anschel, I. Caspi, and S. Mannor, “End-to-end differentiable adversarial imitation learning,” in International Conference on Machine Learning . PMLR, 2017, pp. 390–399
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.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Wang, C. Devin, Q.-Z. Cai, P. Krähenbühl, and T. Darrell, “Monocular plan view networks for autonomous driving,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 2876–2883
2019
Earlier work this paper cites.
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-end interpretable neural motion planner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8660–8669
2019
Earlier work this paper cites.
F. Codevilla, E. Santana, A. M. López, and A. Gaidon, “Exploring the limitations of behavior cloning for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9329–9338
2019
Earlier work this paper cites.
C. Wen, J. Lin, T. Darrell, D. Jayaraman, and Y. Gao, “Fighting copycat agents in behavioral cloning from observation histories,” Advances in Neural Information Processing Systems , vol. 33, pp. 2564–2575, 2020
2020
Earlier work this paper cites.
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” in Conference on Robot Learning . PMLR, 2020, pp. 66–75
2020
Earlier work this paper cites.
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann, “Shortcut learning in deep neural networks,” Nature Machine Intelligence , vol. 2, no. 11, pp. 665–673, 2020
2020
Earlier work this paper cites.
H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, J. Schneider, D. Bradley, and N. Djuric, “Deep kinematic models for kinematically feasible vehicle trajectory predictions,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 10 563–10 569
2020
Earlier work this paper cites.
J. Zhou, R. Wang, X. Liu, Y. Jiang, S. Jiang, J. Tao, J. Miao, and S. Song, “Exploring imitation learning for autonomous driving with feedback synthesizer and differentiable rasterization,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1450–1457
2021
Earlier work this paper cites.
H. Caesar, J. Kabzan, K. S. Tan, W. K. Fong, E. Wolff, A. Lang, L. Fletcher, O. Beijbom, and S. Omari, “nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops , 2021
2021
Earlier work this paper cites.
K. Chitta, A. Prakash, and A. Geiger, “Neat: Neural attention fields for end-to-end autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 793–15 803
2021
Cited alongside, same era.
J. Cheng, Y. Chen, Q. Zhang, L. Gan, C. Liu, and M. Liu, “Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8999–9005
2022
Cited alongside, same era.
M. Vitelli, Y. Chang, Y. Ye, A. Ferreira, M. Wołczyk, B. Osiński, M. Niendorf, H. Grimmett, Q. Huang, A. Jain, et al. , “Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 897–904
2022
Cited alongside, same era.
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
S. Pini, C. S. Perone, A. Ahuja, A. S. R. Ferreira, M. Niendorf, and S. Zagoruyko, “Safe real-world autonomous driving by learning to predict and plan with a mixture of experts,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 10 069–10 075
2023
Closest in time.
2023
Closest in time.
Z. Huang, H. Liu, J. Wu, and C. Lv, “Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving,” IEEE transactions on neural networks and learning systems , 2023
2023
Closest in time.
2023
Closest in time.
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2022
Cited alongside, same era.
J. Cheng, R. Xin, S. Wang, and M. Liu, “Mpnp: Multi-policy neural planner for urban driving,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 10 549–10 554
2022
Cited alongside, same era.
S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao, “St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,” in European Conference on Computer Vision . Springer, 2022, pp. 533–549
2022
Cited alongside, same era.
D. Chen and P. Krähenbühl, “Learning from all vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 222–17 231
2022
Cited alongside, same era.
K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger, “Transfuser: Imitation with transformer-based sensor fusion for autonomous driving,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Cited alongside, same era.
Q. Zhang, M. Tang, R. Geng, F. Chen, R. Xin, and L. Wang, “Mmfn: Multi-modal-fusion-net for end-to-end driving,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 8638–8643
2022
Cited alongside, same era.
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 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2445–2451
2022
Cited alongside, same era.
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
2022
Cited alongside, same era.
Y. Lu, J. Fu, G. Tucker, X. Pan, E. Bronstein, B. Roelofs, B. Sapp, B. White, A. Faust, S. Whiteson, et al. , “Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios,” NeurIPS 2022 Workshop on Machine Learning for Autonomous Driving , 2022
2022
Cited alongside, same era.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, et al. , “Planning-oriented autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 853–17 862
2023
Closest in time.
2023
Closest in time.
H. Shao, L. Wang, R. Chen, H. Li, and Y. Liu, “Safety-enhanced autonomous driving using interpretable sensor fusion transformer,” in Conference on Robot Learning . PMLR, 2023, pp. 726–737
2023
Closest in time.
X. Jia, P. Wu, L. Chen, J. Xie, C. He, J. Yan, and H. Li, “Think twice before driving: Towards scalable decoders for end-to-end autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 983–21 994
2023
Closest in time.
2023
Closest in time.
Z. Wu, T. Liu, L. Luo, Z. Zhong, J. Chen, H. Xiao, C. Hou, H. Lou, Y. Chen, R. Yang, Y. Huang, X. Ye, Z. Yan, Y. Shi, Y. Liao, and H. Zhao, “Mars: An instance-aware, modular and realistic simulator for autonomous driving,” CICAI , 2023
2023
Closest in time.
Y. Hu, K. Li, P. Liang, J. Qian, Z. Yang, H. Zhang, W. Shao, Z. Ding, W. Xu, and Q. Liu, “Imitation with spatial-temporal heatmap: 2nd place solution for nuplan challenge,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops , 2023
2023
Closest in time.
K. Renz, K. Chitta, O.-B. Mercea, A. S. Koepke, Z. Akata, and A. Geiger, “Plant: Explainable planning transformers via object-level representations,” in Conference on Robot Learning . PMLR, 2023, pp. 459–470
2023
Closest in time.
W. Xi, L. Shi, and G. Cao, “An imitation learning method with data augmentation and post processing for planning in autonomous driving,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops , 2023
2023
Closest in time.
2023
Closest in time.
J. Cheng, X. Mei, and M. Liu, “Forecast-MAE: Self-supervised pre-training for motion forecasting with masked autoencoders,” Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
Closest in time.
D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with misconceptions about learning-based vehicle motion planning,” in Conference on Robot Learning (CoRL) , 2023
2023
Closest in time.