Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

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.

J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141

2018

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 , 2020, pp. 11 621–11 631

2020

Earlier work this paper cites.

J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” arXiv preprint arXiv:2001.08361 , 2020

Original

2020

Earlier work this paper cites.

T. Henighan, J. Kaplan, M. Katz, M. Chen, C. Hesse, J. Jackson, H. Jun, T. B. Brown, P. Dhariwal, S. Gray et al. , “Scaling laws for autoregressive generative modeling,” arXiv preprint arXiv:2010.14701 , 2020

Original

2020

Earlier work this paper cites.

J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 194–210

2020

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,” arXiv preprint arXiv:2106.11810 , 2021

Original

2021

Earlier work this paper cites.

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 , vol. 45, no. 11, pp. 12 878–12 895, 2022

2022

Earlier work this paper cites.

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

Earlier work this paper cites.

X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer, “Scaling vision transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 12 104–12 113

2022

Earlier work this paper cites.

J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: a visual language model for few-shot learning,” Advances in neural information processing systems , vol. 35, pp. 23 716–23 736, 2022

2022

Earlier work this paper cites.

B. Liao, S. Chen, X. Wang, T. Cheng, Q. Zhang, W. Liu, and C. Huang, “Maptr: Structured modeling and learning for online vectorized hd map construction,” arXiv preprint arXiv:2208.14437 , 2022

Original

2022

Earlier work this paper cites.

A.-Q. Cao and R. De Charette, “Monoscene: Monocular 3d semantic scene completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3991–4001

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

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 , 2023, pp. 8340–8350

2023

Cited alongside, same era.

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

Anonymous, “Drivetransformer: Unified transformer for scalable end-to-end autonomous driving,” in Submitted to The Thirteenth International Conference on Learning Representations , 2024, under review. [Online]. Available: https://openreview.net/forum?id=M42KR4W9P5

2024

Closest in time.

X. Jia, Z. Yang, Q. Li, Z. Zhang, and J. Yan, “Bench2drive: Towards multi-ability benchmarking of closed-loop end-to-end autonomous driving,” arXiv preprint arXiv:2406.03877 , 2024

Original

2024

Closest in time.

X. Weng, B. Ivanovic, Y. Wang, Y. Wang, and M. Pavone, “Para-drive: Parallelized architecture for real-time autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 449–15 458

2024

Closest in time.

W. Sun, X. Lin, Y. Shi, C. Zhang, H. Wu, and S. Zheng, “Sparsedrive: End-to-end autonomous driving via sparse scene representation,” arXiv preprint arXiv:2405.19620 , 2024

Original

2024