Fetching the paper…
Reading the bibliography…
Ensuring safe, comfortable, and efficient planning is crucial for autonomous driving systems.
N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich, “Maximum margin planning,” in Proceedings of the 23rd international conference on Machine learning , 2006, pp. 729–736
2006
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.
2017
Earlier work this paper cites.
F. Codevilla, A. M. Lopez, and et al., “End-to-end driving via conditional imitation learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1–10
2018
Earlier work this paper cites.
T. Shi, P. Wang, X. Cheng, C.-Y. Chan, and D. Huang, “Driving decision and control for automated lane change behavior based on deep reinforcement learning,” in 2019 IEEE intelligent transportation systems conference (ITSC) . IEEE, 2019, pp. 2895–2900
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. Lopez, and A. Gaidon, “Exploring the limitations of behavior cloning for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019
2019
Earlier work this paper cites.
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 (CVPR) , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
D. Chen, B. Zhou, V. Koltun, and P. Krahenbuhl, “Learning by cheating,” in Conference on Robot Learning . PMLR, 2020, pp. 66–75
2020
Earlier work this paper cites.
B. R. Kiran, I. Sobh, V. Talpaert, P. Mannion, A. A. Al Sallab, S. Yogamani, and P. Pérez, “Deep reinforcement learning for autonomous driving: A survey,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 6, pp. 4909–4926, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
P. Hu, A. Huang, J. Dolan, D. Held, and D. Ramanan, “Safe local motion planning with self-supervised freespace forecasting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 732–12 741
2021
Earlier work this paper cites.
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 (ICCV) , 2021
2021
Earlier work this paper cites.
W. Zhou, Z. Cao, N. Deng, X. Liu, K. Jiang, and D. Yang, “Dynamically conservative self-driving planner for long-tail cases,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 3, pp. 3476–3488, 2022
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
T. Khurana, P. Hu, A. Dave, J. Ziglar, D. Held, and D. Ramanan, “Differentiable raycasting for self-supervised occupancy forecasting,” in European Conference on Computer Vision . Springer, 2022, pp. 353–369
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 Proceedings of the European Conference on Computer Vision (ECCV) , 2022
2024
Later among the works it cites.
H. X. Liu and S. Feng, “Curse of rarity for autonomous vehicles,” nature communications , vol. 15, no. 1, p. 4808, 2024
2024
Later among the works it cites.
H. Shao, Y. Hu, L. Wang, G. Song, S. L. Waslander, Y. Liu, and H. Li, “Lmdrive: Closed-loop end-to-end driving with large language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 120–15 130
2024
Later among the works it cites.
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q. Zhang, W. Liu, C. Huang, and X. Wang, “Vad: Vectorized scene representation for efficient autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 8340–8350
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
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 (CVPR) , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
R. Tan, S. Lou, Y. Zhou, and C. Lv, “Multi-modal llm-enabled long-horizon skill learning for robotic manipulation,” in 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM) , 2024, pp. 14–19
2024
Later among the works it cites.
X. Tian, J. Gu, B. Li, Y. Liu, Y. Wang, Z. Zhao, K. Zhan, P. Jia, X. Lang, and H. Zhao, “Drivevlm: The convergence of autonomous driving and large vision-language models,” in Conference on Robot Learning (CoRL) , 2024, * Equal contribution. Listing order is random. † Corresponding author
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
K. Feng, C. Li, D. Ren, Y. Yuan, and G. Wang, “On the road to portability: Compressing end-to-end motion planner for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 099–15 108
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu et al. , “Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 24 185–24 198
2024
Later among the works it cites.