PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving

Bolte, J.A., et al.: Towards corner case detection for autonomous driving. In: 2019 IEEE Intelligent vehicles symposium (IV). pp. 438–445 (2019)

2019

Earlier work this paper cites.

Brown, T., et al.: Language models are few-shot learners. In: Advances in neural information processing systems. vol. 33, pp. 1877–1901 (2020)

2020

Earlier work this paper cites.

Caesar, H., et al.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11621–11631 (2020)

2020

Earlier work this paper cites.

Gao, T., et al.: Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723 (2020)

Original

2020

Earlier work this paper cites.

Li, P., et al.: A survey on deep learning-based approaches for autonomous driving. IEEE Transactions on Intelligent Transportation Systems (2020)

2020

Earlier work this paper cites.

Bogdoll, D., et al.: Description of corner cases in automated driving: Goals and challenges. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1023–1028 (2021)

2021

Earlier work this paper cites.

Hu, E., et al.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)

Original

2021

Earlier work this paper cites.

Liu, P., et al.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (2021)

Original

2021

Earlier work this paper cites.

Kojima, T., et al.: Large language models are zero-shot reasoners. arXiv preprint arXiv:2205.11916 (2022)

Original

2022

Earlier work this paper cites.

Kojima, T., et al.: Large language models are zero-shot reasoners. In: Advances in neural information processing systems. vol. 35, pp. 22199–22213 (2022)

2022

Earlier work this paper cites.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E.H., Le, Q.V., et al.: Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903 (2022)

Original

2022

Earlier work this paper cites.

Bai, J., et al.: Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966 (2023)

Original

2023

Earlier work this paper cites.

Chen, L., et al.: End-to-end autonomous driving: Challenges and frontiers. arXiv preprint arXiv:2306.16927 (2023)

Original

2023

Earlier work this paper cites.

Chen, L., et al.: Driving with llms: Fusing object-level vector modality for explainable autonomous driving. arXiv preprint arXiv:2310.01957 (2023)

Original

2023

Earlier work this paper cites.

Contributors: Drivelm: Drive on language (2023)

2023

Cited alongside, same era.

Kochdumper, N., Bak, S.: Real-time capable decision making for autonomous driving using reachable sets. arXiv preprint arXiv:2309.12289 (2023)

Original

2023

Cited alongside, same era.

Kong, A., et al.: Better zero-shot reasoning with role-play prompting. arXiv preprint arXiv:2308.07702 (2023)

Original

2023

Cited alongside, same era.

Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown (2023)

2023

Cited alongside, same era.

Li, B., Li, F., Gao, S., Fan, Q., Lu, Y., Hu, R., Zhao, Z.: Efficient prompt tuning for vision and language models. In: International Conference on Neural Information Processing. pp. 77–89. Springer (2023)

2023

Cited alongside, same era.

Wang, W., et al.: Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving. arXiv preprint arXiv:2312.09245 (2023)

Original

2023

Later among the works it cites.

Wen, L., et al.: Dilu: A knowledge-driven approach to autonomous driving with large language models. arXiv preprint arXiv:2309.16292 (2023)

Original

2023

Later among the works it cites.

Xia, Y., et al.: Parameterized decision-making with multi-modal perception for autonomous driving. arXiv preprint arXiv:2312.11935 (2023)

Original

2023

Later among the works it cites.

Xu, Z., et al.: Drivegpt4: Interpretable end-to-end autonomous driving via large language model. arXiv preprint arXiv:2310.01412 (2023)

Original

2023

Later among the works it cites.