AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving

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.

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

Earlier work this paper cites.

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

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,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

2024

Earlier work this paper cites.

J. Zhang, J. Huang, S. Jin, and S. Lu, “Vision-language models for vision tasks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

2024

Earlier work this paper cites.

J.-J. Hwang, R. Xu, H. Lin, W.-C. Hung, J. Ji, K. Choi, D. Huang, T. He, P. Covington, B. Sapp et al. , “Emma: End-to-end multimodal model for autonomous driving,” arXiv preprint arXiv:2410.23262 , 2024

Original

2024

Earlier work this paper 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,” arXiv preprint arXiv:2402.12289 , 2024

Original

2024

Earlier work this paper cites.

B. Jiang, S. Chen, B. Liao, X. Zhang, W. Yin, Q. Zhang, C. Huang, W. Liu, and X. Wang, “Senna: Bridging large vision-language models and end-to-end autonomous driving,” arXiv preprint arXiv:2410.22313 , 2024

Original

2024

Earlier work this paper cites.

D. Dauner, M. Hallgarten, T. Li, X. Weng, Z. Huang, Z. Yang, H. Li, I. Gilitschenski, B. Ivanovic, M. Pavone et al. , “Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,” Advances in Neural Information Processing Systems , vol. 37, pp. 28 706–28 719, 2024

2024

Earlier work this paper cites.

A.-M. Marcu, L. Chen, J. Hünermann, A. Karnsund, B. Hanotte, P. Chidananda, S. Nair, V. Badrinarayanan, A. Kendall, J. Shotton et al. , “Lingoqa: Visual question answering for autonomous driving,” in European Conference on Computer Vision . Springer, 2024, pp. 252–269

2024

Earlier work this paper cites.

C. Sima, K. Renz, K. Chitta, L. Chen, H. Zhang, C. Xie, J. Beißwenger, P. Luo, A. Geiger, and H. Li, “Drivelm: Driving with graph visual question answering,” in European conference on computer vision . Springer, 2024, pp. 256–274

2024

Earlier work this paper cites.

T. Qian, J. Chen, L. Zhuo, Y. Jiao, and Y.-G. Jiang, “Nuscenes-qa: A multi-modal visual question answering benchmark for autonomous driving scenario,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 5, 2024, pp. 4542–4550

2024

Earlier work this paper cites.

X. Ding, J. Han, H. Xu, X. Liang, W. Zhang, and X. Li, “Holistic autonomous driving understanding by bird’s-eye-view injected multi-modal large models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 13 668–13 677

2024

Earlier work this paper cites.

Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Y. Li, Y. Wu et al. , “Deepseekmath: Pushing the limits of mathematical reasoning in open language models,” arXiv preprint arXiv:2402.03300 , 2024

Original

2024

Earlier work this paper cites.

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

Cited alongside, same era.

C. Yuan, Z. Zhang, J. Sun, S. Sun, Z. Huang, C. D. W. Lee, D. Li, Y. Han, A. Wong, K. P. Tee et al. , “Drama: An efficient end-to-end motion planner for autonomous driving with mamba,” arXiv preprint arXiv:2408.03601 , 2024

Original

2024

Cited alongside, same era.

Z. Li, K. Li, S. Wang, S. Lan, Z. Yu, Y. Ji, Z. Li, Z. Zhu, J. Kautz, Z. Wu et al. , “Hydra-mdp: End-to-end multimodal planning with multi-target hydra-distillation,” arXiv preprint arXiv:2406.06978 , 2024

Original

2024

Cited alongside, same era.

S. Jiang, Z. Huang, K. Qian, Z. Luo, T. Zhu, Y. Zhong, Y. Tang, M. Kong, Y. Wang, S. Jiao et al. , “A survey on vision-language-action models for autonomous driving,” arXiv preprint arXiv:2506.24044 , 2025

Original

2025

D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi et al. , “Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,” arXiv preprint arXiv:2501.12948 , 2025

Original

2025

Closest in time.

J. Zhang, N. Lin, L. Hou, L. Feng, and J. Li, “Adaptthink: Reasoning models can learn when to think,” arXiv preprint arXiv:2505.13417 , 2025

Original

2025

Closest in time.

M. Fatemi, B. Rafiee, M. Tang, and K. Talamadupula, “Concise reasoning via reinforcement learning,” arXiv preprint arXiv:2504.05185 , 2025

Original

2025

Closest in time.

J. Yi, J. Wang, and S. Li, “Shorterbetter: Guiding reasoning models to find optimal inference length for efficient reasoning,” arXiv preprint arXiv:2504.21370 , 2025

Original

2025

Closest in time.