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We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or highly realistic simulation.
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2017
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F. Codevilla, M. Müller, A. López, V. Koltun, and A. Dosovitskiy, “End-to-end driving via conditional imitation learning,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 4693–4700
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2018
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2018
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E. Leurent, “An environment for autonomous driving decision-making,” https://github.com/eleurent/highway-env, 2018
2018
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H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
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S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 403–14 412
2021
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A. Cui, S. Casas, A. Sadat, R. Liao, and R. Urtasun, “Lookout: Diverse multi-future prediction and planning for self-driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 107–16 116
2021
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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
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2019
Cited alongside, same era.
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
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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
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A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun, “Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,” in European Conference on Computer Vision . Springer, 2020, pp. 414–430
2020
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M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in European Conference on Computer Vision . Springer, 2020, pp. 541–556
2020
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2021
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2021
Later among the works it cites.
2021
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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
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Igl, Kim, Kuefler, Mougin, Shah, Shiarlis, Anguelov, Palatucci, White, and Whiteson, “Symphony: Learning realistic and diverse agents for autonomous driving simulation,” May 2022
2022
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H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric, “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2090–2096
2096
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