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Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” Advances in neural information processing systems , vol. 1, 1988
1988
Earlier work this paper cites.
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.
S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann et al. , “Stanley: The robot that won the darpa grand challenge,” Journal of field Robotics , vol. 23, no. 9, pp. 661–692, 2006
2006
Earlier work this paper cites.
A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model mobil for car-following models,” Transportation Research Record , vol. 1999, no. 1, pp. 86–94, 2007
2007
Earlier work this paper cites.
A. Bacha, C. Bauman, R. Faruque, M. Fleming, C. Terwelp, C. Reinholtz, D. Hong, A. Wicks, T. Alberi, D. Anderson et al. , “Odin: Team victortango’s entry in the darpa urban challenge,” Journal of field Robotics , vol. 25, no. 8, pp. 467–492, 2008
2008
Earlier work this paper cites.
J. Leonard, J. How, S. Teller, M. Berger, S. Campbell, G. Fiore, L. Fletcher, E. Frazzoli, A. Huang, S. Karaman et al. , “A perception-driven autonomous urban vehicle,” Journal of Field Robotics , vol. 25, no. 10, pp. 727–774, 2008
2008
Earlier work this paper cites.
C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer et al. , “Autonomous driving in urban environments: Boss and the urban challenge,” Journal of field Robotics , vol. 25, no. 8, pp. 425–466, 2008
2008
Earlier work this paper cites.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2722–2730
2015
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.
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
2018
Earlier work this paper cites.
A. Sauer, N. Savinov, and A. Geiger, “Conditional affordance learning for driving in urban environments,” in Conference on Robot Learning . PMLR, 2018, pp. 237–252
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
F. Codevilla, A. M. Lopez, V. Koltun, and A. Dosovitskiy, “On offline evaluation of vision-based driving models,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 236–251
2018
Earlier work this paper cites.
G. Bagschik, T. Menzel, and M. Maurer, “Ontology based scene creation for the development of automated vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1813–1820
2018
Earlier work this paper cites.
T. Menzel, G. Bagschik, and M. Maurer, “Scenarios for development, test and validation of automated vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1821–1827
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.
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.
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.
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.
W. Ding, B. Chen, M. Xu, and D. Zhao, “Learning to collide: An adaptive safety-critical scenarios generating method,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2243–2250
2020
Cited alongside, same era.
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
Cited alongside, same era.
J. Kim, S. Moon, A. Rohrbach, T. Darrell, and J. Canny, “Advisable learning for self-driving vehicles by internalizing observation-to-action rules,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9661–9670
2020
Cited alongside, same era.
2023
Later among the works it cites.
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
Later among the works it cites.
H. Shao, L. Wang, R. Chen, S. L. Waslander, H. Li, and Y. Liu, “Reasonnet: End-to-end driving with temporal and global reasoning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 13 723–13 733
2023
Later among the works it cites.
M. Hallgarten, M. Stoll, and A. Zell, “From prediction to planning with goal conditioned lane graph traversals,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2023, pp. 951–958
2023
Later among the works it cites.
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2021
Cited alongside, same era.
N. Rhinehart, J. He, C. Packer, M. A. Wright, R. McAllister, J. E. Gonzalez, and S. Levine, “Contingencies from observations: Tractable contingency planning with learned behavior models,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 663–13 669
2021
Cited alongside, same era.
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.
W. Ding, B. Chen, B. Li, K. J. Eun, and D. Zhao, “Multimodal safety-critical scenarios generation for decision-making algorithms evaluation,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1551–1558, 2021
2021
Cited alongside, same era.
J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun, “Advsim: Generating safety-critical scenarios for self-driving vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9909–9918
2021
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.
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.
P. Wu, X. Jia, L. Chen, J. Yan, H. Li, and Y. Qiao, “Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline,” Advances in Neural Information Processing Systems , vol. 35, pp. 6119–6132, 2022
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
2022
Cited alongside, same era.
Z. Huang, H. Liu, and C. Lv, “Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3903–3913
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
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
Later among the works it cites.
2023
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2023
Later among the works it cites.
2023
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B. Jin, X. Liu, Y. Zheng, P. Li, H. Zhao, T. Zhang, Y. Zheng, G. Zhou, and J. Liu, “Adapt: Action-aware driving caption transformer,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7554–7561
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
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2023
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2023
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D. Fu, X. Li, L. Wen, M. Dou, P. Cai, B. Shi, and Y. Qiao, “Drive like a human: Rethinking autonomous driving with large language models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 910–919
2024
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T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “Qlora: Efficient finetuning of quantized llms,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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