Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving

J. Pickands III, “Statistical inference using extreme order statistics,” the Annals of Statistics , pp. 119–131, 1975

1975

Earlier work this paper cites.

J. R. Hosking and J. R. Wallis, “Parameter and quantile estimation for the generalized pareto distribution,” Technometrics , vol. 29, no. 3, pp. 339–349, 1987

1987

Earlier work this paper cites.

S. Coles, J. Bawa, L. Trenner, and P. Dorazio, An introduction to statistical modeling of extreme values . Springer, 2001, vol. 208

2001

Earlier work this paper cites.

J. Langford and J. Shawe-Taylor, “Pac-bayes & margins,” Advances in neural information processing systems , vol. 15, 2002

2002

Earlier work this paper cites.

W. G. Najm, J. D. Smith, M. Yanagisawa, A. John et al. , “Pre-crash scenario typology for crash avoidance research,” United States. Department of Transportation. National Highway Traffic Safety …, Tech. Rep., 2007

2007

Earlier work this paper cites.

D. González, J. Pérez, V. Milanés, and F. Nashashibi, “A review of motion planning techniques for automated vehicles,” IEEE Transactions on intelligent transportation systems , vol. 17, no. 4, pp. 1135–1145, 2015

2015

Earlier work this paper cites.

L. Janson, E. Schmerling, and M. Pavone, “Monte carlo motion planning for robot trajectory optimization under uncertainty,” in Robotics Research: Volume 2 . Springer, 2017, pp. 343–361

2017

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.

E. Liang, R. Liaw, R. Nishihara, P. Moritz, R. Fox, K. Goldberg, J. Gonzalez, M. Jordan, and I. Stoica, “Rllib: Abstractions for distributed reinforcement learning,” in International conference on machine learning . PMLR, 2018, pp. 3053–3062

2018

Earlier work this paper cites.

J.-A. Bolte, A. Bar, D. Lipinski, and T. Fingscheidt, “Towards corner case detection for autonomous driving,” in 2019 IEEE Intelligent vehicles symposium (IV) . IEEE, 2019, pp. 438–445

2019

Earlier work this paper cites.

J. Chen, B. Yuan, and M. Tomizuka, “Model-free deep reinforcement learning for urban autonomous driving,” in 2019 IEEE intelligent transportation systems conference (ITSC) . IEEE, 2019, pp. 2765–2771

2019

Earlier work this paper cites.

S. Aradi, “Survey of deep reinforcement learning for motion planning of autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 2, pp. 740–759, 2020

2020

Earlier work this paper cites.

A. Amini, W. Schwarting, A. Soleimany, and D. Rus, “Deep evidential regression,” Advances in neural information processing systems , vol. 33, pp. 14 927–14 937, 2020

2020

Earlier work this paper cites.

A. Wachi and Y. Sui, “Safe reinforcement learning in constrained markov decision processes,” in International Conference on Machine Learning . PMLR, 2020, pp. 9797–9806

2020

Earlier work this paper cites.

B. Charpentier, D. Zügner, and S. Günnemann, “Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts,” Advances in neural information processing systems , vol. 33, pp. 1356–1367, 2020

2020

Earlier work this paper cites.

H. Sun, S. Feng, X. Yan, and H. X. Liu, “Corner case generation and analysis for safety assessment of autonomous vehicles,” Transportation research record , vol. 2675, no. 11, pp. 587–600, 2021

2021

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.

D. Chen, V. Koltun, and P. Krähenbühl, “Learning to drive from a world on rails,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 590–15 599

2021

Earlier work this paper cites.

Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool, “End-to-end urban driving by imitating a reinforcement learning coach,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 15 222–15 232

2021

Earlier work this paper cites.

Z. Cao, S. Xu, H. Peng, D. Yang, and R. Zidek, “Confidence-aware reinforcement learning for self-driving cars,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 7, pp. 7419–7430, 2021

2021

Earlier work this paper cites.

E. Verwimp, M. De Lange, and T. Tuytelaars, “Rehearsal revealed: The limits and merits of revisiting samples in continual learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9385–9394

2021

Earlier work this paper cites.

L. Chen, Y. Li, C. Huang, B. Li, Y. Xing, D. Tian, L. Li, Z. Hu, X. Na, Z. Li et al. , “Milestones in autonomous driving and intelligent vehicles: Survey of surveys,” IEEE Transactions on Intelligent Vehicles , 2022

2022

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.

H. Liu, Z. Huang, J. Wu, and C. Lv, “Improved deep reinforcement learning with expert demonstrations for urban autonomous driving,” in 2022 IEEE intelligent vehicles symposium (IV) . IEEE, 2022, pp. 921–928

2022

Earlier work this paper cites.

Z. Huang, J. Wu, and C. Lv, “Efficient deep reinforcement learning with imitative expert priors for autonomous driving,” IEEE Transactions on Neural Networks and Learning Systems , vol. 34, no. 10, pp. 7391–7403, 2022

2022

Earlier work this paper cites.

Z. Huang, H. Liu, J. Wu, and C. Lv, “Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving,” arXiv preprint arXiv:2207.10422 , 2022

Original

2022

Earlier work this paper cites.

Y. Wu, A. Bighashdel, G. Chen, G. Dubbelman, and P. Jancura, “Continual pedestrian trajectory learning with social generative replay,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 848–855, 2022

2022

Earlier work this paper cites.

E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models.” ICLR , vol. 1, no. 2, p. 3, 2022

2022

Earlier work this paper cites.

Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” in European conference on computer vision . Springer, 2022, pp. 1–18

2022

Earlier work this paper cites.

Y. Zhang, Z. Zhu, W. Zheng, J. Huang, G. Huang, J. Zhou, and J. Lu, “Beverse: Unified perception and prediction in birds-eye-view for vision-centric autonomous driving,” arXiv preprint arXiv:2205.09743 , 2022

Original

2022

Earlier work this paper cites.

A. K. Akan and F. Güney, “Stretchbev: Stretching future instance prediction spatially and temporally,” in European Conference on Computer Vision . Springer, 2022, pp. 444–460

2022

Earlier work this paper cites.

K. Binici, N. T. Pham, T. Mitra, and K. Leman, “Preventing catastrophic forgetting and distribution mismatch in knowledge distillation via synthetic data,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2022, pp. 663–671

2022

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 , 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.

L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li, “End-to-end autonomous driving: Challenges and frontiers,” arXiv preprint arXiv:2306.16927 , 2023

Original

2023

Earlier work this paper 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

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,” arXiv preprint arXiv:2303.12077 , 2023

Original

2023

Cited alongside, same era.

S. Feng, H. Sun, X. Yan, H. Zhu, Z. Zou, S. Shen, and H. X. Liu, “Dense reinforcement learning for safety validation of autonomous vehicles,” Nature , vol. 615, no. 7953, pp. 620–627, 2023

2023

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,” arXiv preprint arXiv:2303.05760 , 2023

Original

2023

Cited alongside, same era.

H. Liu, Z. Huang, X. Mo, and C. Lv, “Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving,” IEEE Transactions on Intelligent Vehicles , 2024

2024

Later among the works it cites.

J. Zhang, Z. Huang, A. Ray, and E. Ohn-Bar, “Feedback-guided autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 000–15 011

2024

Later among the works it cites.

T.-H. Wu, L. Lian, J. E. Gonzalez, B. Li, and T. Darrell, “Self-correcting llm-controlled diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 6327–6336

2024

Later among the works it cites.

H. Lu, X. Jia, Y. Xie, W. Liao, X. Yang, and J. Yan, “Activead: Planning-oriented active learning for end-to-end autonomous driving,” arXiv preprint arXiv:2403.02877 , 2024

Original

2024

Later among the works it cites.