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Motion planners are essential for the safe operation of automated vehicles across various scenarios.
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2020
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2020
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C. Pek, V. Rusinov, S. Manzinger, M. C. Üste, and M. Althoff, “CommonRoad Drivability Checker: Simplifying the development and validation of motion planning algorithms,” in Proc. of the IEEE Intell. Veh. Symp. , 2020, pp. 1013–1020
2020
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M. Klischat, E. I. Liu, F. Holtke, and M. Althoff, “Scenario factory: Creating safety-critical traffic scenarios for automated vehicles,” in Proc. of the IEEE Int. Conf. on Intell. Transp. Syst. , 2020, pp. 1–7
2020
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2021
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2021
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N. Jiang, T. Lutellier, and L. Tan, “CURE: Code-aware neural machine translation for automatic program repair,” in Proc. of the IEEE/ACM Int. Conf. on Software Engineering , 2021, pp. 1161–1173
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
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2023
Later among the works it cites.
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2023
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2023
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Cited alongside, same era.
Y. Lin, S. Maierhofer, and M. Althoff, “Sampling-based trajectory repairing for autonomous vehicles,” in Proc. of the IEEE Int. Conf. on Intell. Transp. Syst. , 2021, pp. 572–579
2021
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. , “Training language models to follow instructions with human feedback,” Proc. of the Advances in Neural Info. Processing Syst. , vol. 35, pp. 27 730–27 744, 2022
2022
Cited alongside, same era.
S. Aradi, “Survey of deep reinforcement learning for motion planning of autonomous vehicles,” IEEE Trans. on Intell. Transp. Syst. , vol. 23, no. 2, pp. 740–759, 2022
2022
Cited alongside, same era.
S. Liu and P. Liu, “Benchmarking and optimization of robot motion planning with motion planning pipeline,” Int. J. of Advanced Manufacturing Technology , vol. 118, pp. 949–961, 2022
2022
Cited alongside, same era.
H. Ye, M. Martinez, and M. Monperrus, “Neural program repair with execution-based backpropagation,” in Proc. of the IEEE/ACM Int. Cof. on Software Engineering , 2022, pp. 1506–1518
2022
Cited alongside, same era.
C. S. Xia and L. Zhang, “Less training, more repairing please: Revisiting automated program repair via zero-shot learning,” in Proc. of the ACM Joint European Software Engineering Conf. and Symp. on the Foundations of Software Engineering , 2022, pp. 959–971
2022
Cited alongside, same era.
S. D. Kolak, R. Martins, C. Le Goues, and V. J. Hellendoorn, “Patch generation with language models: Feasibility and scaling behavior,” in Proc. of the Int. Conf. on Learning Representations: Deep Learning for Code Workshop , 2022
2022
Cited alongside, same era.
J. A. Prenner, H. Babii, and R. Robbes, “Can OpenAI’s Codex fix bugs? an evaluation on QuixBugs,” in Proc. of the Int. Workshop on Automated Program Repair , 2022, pp. 69–75
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
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Y. Lin and M. Althoff, “CommonRoad-CriMe: A toolbox for criticality measures of autonomous vehicles,” in Proc. of the IEEE Intell. Veh. Symp. , 2023, pp. 1–8
2023
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Z. Liu, A. Bahety, and S. Song, “REFLECT: Summarizing robot experiences for failure explanation and correction,” in Proc. of the Conferene on Robot Learning , 2023
2023
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2023
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N. Jain, T. Zhang, W.-L. Chiang, J. E. Gonzalez, K. Sen, and I. Stoica, “LLM-assisted code cleaning for training accurate code generators,” in Proc. of the Int. Conf. on Learning Representations , 2024
2024
Closest in time.
A. Madaan, A. Shypula, U. Alon, M. Hashemi, P. Ranganathan, Y. Yang, G. Neubig, and A. Yazdanbakhsh, “Learning performance-improving code edits,” in Proc. of the Int. Conf. on Learning Representations , 2024
2024
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L. Wen, D. Fu, X. Li, X. Cai, T. Ma, P. Cai, M. Dou, B. Shi, L. He, and Y. Qiao, “DiLu: A knowledge-driven approach to autonomous driving with large language models,” in Proc. of the Int. Conf. on Learning Representations , 2024
2024
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C. Sima, K. Renz, K. Chitta, L. Chen, H. Zhang, C. Xie, P. Luo, A. Geiger, and H. Li, “DriveLM: Driving with graph visual question answering,” in Proc. of the European Conf. on Computer Vision , 2024
2024
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C. Cui, Y. Ma, X. Cao, W. Ye, and Z. Wang, “Drive as you speak: Enabling human-like interaction with large language models in autonomous vehicles,” in Proc. of the IEEE/CVF Winter Conf. on Applications of Computer Vision , 2024, pp. 902–909
2024
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L. Chen, O. Sinavski, J. Hünermann, A. Karnsund, A. J. Willmott, D. Birch, D. Maund, and J. Shotton, “Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving,” in Proc. of the IEEE Int. Conf. on Robot. and Autom. , 2024, pp. 14 093–14 100
2024
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H. Shao, Y. Hu, L. Wang, S. L. Waslander, Y. Liu, and H. Li, “LMDrive: Closed-loop end-to-end driving with large language models,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition , 2024
2024
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J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, B. Yin, and X. Hu, “Harnessing the power of LLMs in practice: A survey on ChatGPT and beyond,” ACM Trans. Knowl. Discov. Data , vol. 18, no. 6, pp. 1–32, 2024
2024
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