Asynchronous Large Language Model Enhanced Planner for Autonomous Driving

Kesting, A., Treiber, M., Helbing, D.: General lane-changing model mobil for car-following models. Transportation Research Record: Journal of the Transportation Research Board p. 86–94 (Jan 2007). https://doi.org/10.3141/1999-10, http://dx.doi.org/10.3141/1999-10

1999

Earlier work this paper cites.

Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical Review E p. 1805–1824 (Jul 2002). https://doi.org/10.1103/physreve.62.1805, http://dx.doi.org/10.1103/physreve.62.1805

2002

Earlier work this paper cites.

Bacha, A., Bauman, C., Faruque, R., Fleming, M., Terwelp, C., Reinholtz, C., Hong, D., Wicks, A., Alberi, T., Anderson, D., et al.: Odin: Team victortango’s entry in the darpa urban challenge. Journal of field Robotics 25

2008

Earlier work this paper cites.

Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., et al.: A perception-driven autonomous urban vehicle. Journal of Field Robotics 25

2008

Earlier work this paper cites.

Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al.: Autonomous driving in urban environments: Boss and the urban challenge. Journal of field Robotics 25

2008

Earlier work this paper cites.

Fan, H., Zhu, F., Liu, C., Zhang, L., Zhuang, L., Li, D., Zhu, W., Hu, J., Li, H., Kong, Q.: Baidu apollo em motion planner. arXiv preprint arXiv:1807.08048 (2018)

Original

2018

Earlier work this paper cites.

Baidu: Apollo auto. https://github.com/ApolloAuto/apollo (July 2019)

2019

Earlier work this paper cites.

Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.M., Lam, V.D., Bewley, A., Shah, A.: Learning to drive in a day. In: 2019 International Conference on Robotics and Automation (ICRA). pp. 8248–8254. IEEE (2019)

2019

Earlier work this paper cites.

Caesar, H., Kabzan, J., Tan, K.S., Fong, W.K., Wolff, E., Lang, A., Fletcher, L., Beijbom, O., Omari, S.: nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810 (2021)

Original

2021

Earlier work this paper cites.

Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)

Original

2021

Earlier work this paper cites.

Chen, L., Li, Y., Huang, C., Li, B., Xing, Y., Tian, D., Li, L., Hu, Z., Na, X., Li, Z., et al.: Milestones in autonomous driving and intelligent vehicles: Survey of surveys. IEEE Transactions on Intelligent Vehicles 8

2022

Earlier work this paper cites.

Hu, S., Chen, L., Wu, P., Li, H., Yan, J., Tao, D.: St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In: European Conference on Computer Vision. pp. 533–549. Springer (2022)

2022

Earlier work this paper cites.

Renz, K., Chitta, K., Mercea, O.B., Koepke, A., Akata, Z., Geiger, A.: Plant: Explainable planning transformers via object-level representations. arXiv preprint arXiv:2210.14222 (2022)

Original

2022

Earlier work this paper cites.

Scheel, O., Bergamini, L., Wolczyk, M., Osiński, B., Ondruska, P.: Urban driver: Learning to drive from real-world demonstrations using policy gradients. In: Conference on Robot Learning. pp. 718–728. PMLR (2022)

2022

Earlier work this paper cites.

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

Original

2023

Earlier work this paper cites.

Chen, L., Zhang, Y., Ren, S., Zhao, H., Cai, Z., Wang, Y., Wang, P., Liu, T., Chang, B.: Towards end-to-end embodied decision making via multi-modal large language model: Explorations with gpt4-vision and beyond. arXiv preprint arXiv:2310.02071 (2023)

Original

2023

Earlier work this paper cites.

Chen, L., Sinavski, O., Hünermann, J., Karnsund, A., Willmott, A.J., Birch, D., Maund, D., Shotton, J.: Driving with llms: Fusing object-level vector modality for explainable autonomous driving. arXiv preprint arXiv:2310.01957 (2023)

Original

2023

Cited alongside, same era.

Cui, C., Ma, Y., Cao, X., Ye, W., Wang, Z.: Receive, reason, and react: Drive as you say with large language models in autonomous vehicles. arXiv preprint arXiv:2310.08034 (2023)

Original

2023

Cited alongside, same era.

Dauner, D., Hallgarten, M., Geiger, A., Chitta, K.: Parting with misconceptions about learning-based vehicle motion planning. arXiv preprint arXiv:2306.07962 (2023)

Original

2023

Cited alongside, same era.

Hallgarten, M., Stoll, M., Zell, A.: From prediction to planning with goal conditioned lane graph traversals. arXiv preprint arXiv:2302.07753 (2023)

Original

2023

Cited alongside, same era.

Sha, H., Mu, Y., Jiang, Y., Chen, L., Xu, C., Luo, P., Li, S.E., Tomizuka, M., Zhan, W., Ding, M.: Languagempc: Large language models as decision makers for autonomous driving. arXiv preprint arXiv:2310.03026 (2023)

Original

2023

Later among the works it cites.

Shao, H., Hu, Y., Wang, L., Waslander, S.L., Liu, Y., Li, H.: Lmdrive: Closed-loop end-to-end driving with large language models. arXiv preprint arXiv:2312.07488 (2023)

Original

2023

Later among the works it cites.

Sharan, S., Pittaluga, F., Chandraker, M., et al.: Llm-assist: Enhancing closed-loop planning with language-based reasoning. arXiv preprint arXiv:2401.00125 (2023)

Original

2023

Later among the works it cites.

Sima, C., Renz, K., Chitta, K., Chen, L., Zhang, H., Xie, C., Luo, P., Geiger, A., Li, H.: Drivelm: Driving with graph visual question answering. arXiv preprint arXiv:2312.14150 (2023)

Original

2023

Later among the works it cites.