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The rapid evolution of large language models in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities.
J. S. B. Evans, “Heuristic and analytic processes in reasoning,” British Journal of Psychology , vol. 75, no. 4, pp. 451–468, 1984
1984
Earlier work this paper cites.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E , vol. 62, no. 2, p. 1805, 2000
2000
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
R. Brachman and H. Levesque, Knowledge representation and reasoning . Elsevier, 2004
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , 2005, pp. 65–72
2005
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The international journal of robotics research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “Cider: Consensus-based image description evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4566–4575
2015
Earlier work this paper cites.
B. Kouvaritakis and M. Cannon, “Model predictive control,” Switzerland: Springer International Publishing , vol. 38, no. 13-56, p. 7, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell, “Curiosity-driven exploration by self-supervised prediction,” in International conference on machine learning . PMLR, 2017, pp. 2778–2787
2017
Earlier work this paper cites.
B. Coifman and L. Li, “A critical evaluation of the next generation simulation (ngsim) vehicle trajectory dataset,” Transportation Research Part B: Methodological , vol. 105, pp. 362–377, 2017
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.
M. O’Kelly, A. Sinha, H. Namkoong, R. Tedrake, and J. C. Duchi, “Scalable end-to-end autonomous vehicle testing via rare-event simulation,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
J. Kim, A. Rohrbach, T. Darrell, J. Canny, and Z. Akata, “Textual explanations for self-driving vehicles,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 563–578
2018
Earlier work this paper cites.
R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in 2018 21st international conference on intelligent transportation systems (ITSC) . IEEE, 2018, pp. 2118–2125
2018
Earlier work this paper cites.
W. Li, C. Pan, R. Zhang, J. Ren, Y. Ma, J. Fang, F. Yan, Q. Geng, X. Huang, H. Gong et al. , “Aads: Augmented autonomous driving simulation using data-driven algorithms,” Science robotics , vol. 4, no. 28, p. eaaw0863, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Tang, M. Naphade, M.-Y. Liu, X. Yang, S. Birchfield, S. Wang, R. Kumar, D. Anastasiu, and J.-N. Hwang, “Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 8797–8806
2019
Earlier work this paper cites.
J. Guo, U. Kurup, and M. Shah, “Is it safe to drive? an overview of factors, metrics, and datasets for driveability assessment in autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 8, pp. 3135–3151, 2019
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. A. Theodorou, and B. Boots, “Imitation learning for agile autonomous driving,” The International Journal of Robotics Research , vol. 39, no. 2-3, pp. 286–302, 2020
2020
Earlier work this paper cites.
Y. Shan, B. Zheng, L. Chen, L. Chen, and D. Chen, “A reinforcement learning-based adaptive path tracking approach for autonomous driving,” IEEE Transactions on Vehicular Technology , vol. 69, no. 10, pp. 10 581–10 595, 2020
2020
Earlier work this paper cites.
Y. Xu, X. Yang, L. Gong, H.-C. Lin, T.-Y. Wu, Y. Li, and N. Vasconcelos, “Explainable object-induced action decision for autonomous vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9523–9532
2020
Earlier work this paper cites.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2636–2645
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2446–2454
2020
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.
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.
W. Xiao, N. Mehdipour, A. Collin, A. Y. Bin-Nun, E. Frazzoli, R. D. Tebbens, and C. Belta, “Rule-based optimal control for autonomous driving,” in Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems , 2021, pp. 143–154
2021
Earlier work this paper cites.
W. Liu, Q. Dong, P. Wang, G. Yang, L. Meng, Y. Song, Y. Shi, and Y. Xue, “A survey on autonomous driving datasets,” in 2021 8th International Conference on Dependable Systems and Their Applications (DSA) . IEEE, 2021, pp. 399–407
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.
L. Wang, C. Fernandez, and C. Stiller, “High-level decision making for automated highway driving via behavior cloning,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 1, pp. 923–935, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper 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
Earlier work this paper cites.
2023
Earlier work this paper cites.
L. Zhao, L. Zhang, Z. Wu, Y. Chen, H. Dai, X. Yu, Z. Liu, T. Zhang, X. Hu, X. Jiang et al. , “When brain-inspired ai meets agi,” Meta-Radiology , vol. 1, no. 1, p. 100005, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
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
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
H. Lightman, V. Kosaraju, Y. Burda, H. Edwards, B. Baker, T. Lee, J. Leike, J. Schulman, I. Sutskever, and K. Cobbe, “Let’s verify step by step,” in The Twelfth International Conference on Learning Representations , 2023
2023
Earlier work this paper cites.
S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” Advances in neural information processing systems , vol. 36, pp. 11 809–11 822, 2023
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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
Later among the works it cites.
2024
Later among the works it cites.
M. Liu, E. Yurtsever, J. Fossaert, X. Zhou, W. Zimmer, Y. Cui, B. L. Zagar, and A. C. Knoll, “A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,” IEEE Transactions on Intelligent Vehicles , 2024
2024
Later among the works it 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
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
D. Wu, W. Han, T. Wang, X. Dong, X. Zhang, and J. Shen, “Referring multi-object tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 14 633–14 642
2023
Cited alongside, same era.
S. Malla, C. Choi, I. Dwivedi, J. H. Choi, and J. Li, “Drama: Joint risk localization and captioning in driving,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2023, pp. 1043–1052
2023
Cited alongside, same era.
S. Atakishiyev, M. Salameh, H. Babiker, and R. Goebel, “Explaining autonomous driving actions with visual question answering,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2023, pp. 1207–1214
2023
Cited alongside, same era.
Z. Song, Z. He, X. Li, Q. Ma, R. Ming, Z. Mao, H. Pei, L. Peng, J. Hu, D. Yao et al. , “Synthetic datasets for autonomous driving: A survey,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 1, pp. 1847–1864, 2023
2023
Cited alongside, same era.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, pp. 34 892–34 916, 2023
2023
Cited alongside, same era.
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM computing surveys , vol. 55, no. 12, pp. 1–38, 2023
2023
Cited alongside, same era.
2024
Later among the works it cites.
T. Choudhary, V. Dewangan, S. Chandhok, S. Priyadarshan, A. Jain, A. K. Singh, S. Srivastava, K. M. Jatavallabhula, and K. M. Krishna, “Talk2bev: Language-enhanced bird’s-eye view maps for autonomous driving,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 16 345–16 352
2024
Later among the works it cites.
Y. Inoue, Y. Yada, K. Tanahashi, and Y. Yamaguchi, “Nuscenes-mqa: Integrated evaluation of captions and qa for autonomous driving datasets using markup annotations,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 930–938
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Xu, Y. Zhang, E. Xie, Z. Zhao, Y. Guo, K.-Y. K. Wong, Z. Li, and H. Zhao, “Drivegpt4: Interpretable end-to-end autonomous driving via large language model,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
X. Cao, T. Zhou, Y. Ma, W. Ye, C. Cui, K. Tang, Z. Cao, K. Liang, Z. Wang, J. M. Rehg et al. , “Maplm: A real-world large-scale vision-language benchmark for map and traffic scene understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 819–21 830
2024
Later among the works it cites.
E. Sachdeva, N. Agarwal, S. Chundi, S. Roelofs, J. Li, M. Kochenderfer, C. Choi, and B. Dariush, “Rank2tell: A multimodal driving dataset for joint importance ranking and reasoning,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2024, pp. 7513–7522
2024
Later among the works it 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
Later among the works it cites.
2024
Later among the works it cites.
Q. Li, X. Jia, S. Wang, and J. Yan, “Think2drive: Efficient reinforcement learning by thinking with latent world model for autonomous driving (in carla-v2),” in European Conference on Computer Vision . Springer, 2024, pp. 142–158
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Li, K. Katsumata, E. Javanmardi, and M. Tsukada, “Large language models for human-like autonomous driving: A survey,” in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2024, pp. 439–446
2024
Later among the works it cites.
F. Song, B. Yu, M. Li, H. Yu, F. Huang, Y. Li, and H. Wang, “Preference ranking optimization for human alignment,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 17, 2024, pp. 18 990–18 998
2024
Later among the works it cites.
2024
Later among the works it cites.
2025
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2025
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2025
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2025
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2025
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X. Zhang, K. Wang, T. Hu, and H. Ma, “Enhancing autonomous driving through dual-process learning with behavior and reflection integration,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2025, pp. 1–5
2025
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2025
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2025
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M. Peng, X. Guo, X. Chen, K. Chen, M. Zhu, L. Chen, and F.-Y. Wang, “Lc-llm: Explainable lane-change intention and trajectory predictions with large language models,” Communications in Transportation Research , vol. 5, p. 100170, 2025
2025
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2025
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X. Luo, C. Liu, F. Ding, F. Yang, Y. Zhou, J. Loo, and H. H. Tew, “Senserag: Constructing environmental knowledge bases with proactive querying for llm-based autonomous driving,” in Proceedings of the Winter Conference on Applications of Computer Vision , 2025, pp. 989–996
2025
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2025
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S. Fang, J. Liu, M. Ding, Y. Cui, C. Lv, P. Hang, and J. Sun, “Towards interactive and learnable cooperative driving automation: a large language model-driven decision-making framework,” IEEE Transactions on Vehicular Technology , 2025
2025
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2025
Closest in time.
2025
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S. Xing, C. Qian, Y. Wang, H. Hua, K. Tian, Y. Zhou, and Z. Tu, “Openemma: Open-source multimodal model for end-to-end autonomous driving,” in Proceedings of the Winter Conference on Applications of Computer Vision , 2025, pp. 1001–1009
2025
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2025
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2025
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2025
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2025
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2025
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D. Wu, W. Han, Y. Liu, T. Wang, C.-z. Xu, X. Zhang, and J. Shen, “Language prompt for autonomous driving,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 8, 2025, pp. 8359–8367
2025
Closest in time.
2025
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2025
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M. Li, Z. Cui, Y. Wang, Y. Huang, and H. Chen, “An explainable q q -learning method for longitudinal control of autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems , 2025
2025
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