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Simulation-based testing is crucial for validating autonomous vehicles (AVs), yet existing scenario generation methods either overfit to common driving patterns or operate in an offline, non-interactive manner that fails to expose rare, safety-critical corner cases.
W. Ding, B. Chen, M. Xu, and D. Zhao, “Learning to collide: An adaptive safety-critical scenarios generating method,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2243–2250
2020
Earlier work this paper cites.
J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun, “Advsim: Generating safety-critical scenarios for self-driving vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9909–9918
2021
Earlier work this paper cites.
S. Tan, K. Wong, S. Wang, S. Manivasagam, M. Ren, and R. Urtasun, “Scenegen: Learning to generate realistic traffic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 892–901
2021
Earlier work this paper cites.
S. Choi, J. Kim, and H. Yeo, “Trajgail: Generating urban vehicle trajectories using generative adversarial imitation learning,” Transportation Research Part C: Emerging Technologies , vol. 128, p. 103091, 2021
2021
Earlier work this paper cites.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, Z. Yang, A. Chouard, P. Sun, J. Ngiam, V. Vasudevan, A. McCauley, J. Shlens, and D. Anguelov, “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 9710–9719
2021
Earlier work this paper cites.
D. Rempe, J. Philion, L. J. Guibas, S. Fidler, and O. Litany, “Generating useful accident-prone driving scenarios via a learned traffic prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 305–17 315
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.
Z. Zhou, L. Ye, J. Wang, K. Wu, and K. Lu, “Hivt: Hierarchical vector transformer for multi-agent motion prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 8823–8833
2022
Earlier work this paper cites.
L. Feng, Q. Li, Z. Peng, S. Tan, and B. Zhou, “Trafficgen: Learning to generate diverse and realistic traffic scenarios,” in 2023 IEEE international conference on robotics and automation (ICRA) . IEEE, 2023, pp. 3567–3575
2023
Earlier work this paper cites.
E. Pronovost, M. R. Ganesina, N. Hendy, Z. Wang, A. Morales, K. Wang, and N. Roy, “Scenario diffusion: Controllable driving scenario generation with diffusion,” Advances in Neural Information Processing Systems , vol. 36, pp. 68 873–68 894, 2023
2023
Cited alongside, same era.
W. Ding, C. Xu, M. Arief, H. Lin, B. Li, and D. Zhao, “A survey on safety-critical driving scenario generation—a methodological perspective,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 7, pp. 6971–6988, 2023
2023
Cited alongside, same era.
L. Zhang, Z. Peng, Q. Li, and B. Zhou, “Cat: Closed-loop adversarial training for safe end-to-end driving,” in Conference on Robot Learning . PMLR, 2023, pp. 2357–2372
2023
Cited alongside, same era.
K. Hao, W. Cui, Y. Luo, L. Xie, Y. Bai, J. Yang, S. Yan, Y. Pan, and Z. Yang, “Adversarial safety-critical scenario generation using naturalistic human driving priors,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Cited alongside, same era.
J. Zhang, C. Xu, and B. Li, “Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 459–15 469
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Li, F. Zeng, C. Han, and S. Feng, “Vehicle lane-changing scenario generation using time-series generative adversarial networks with an adaptative parameter optimization strategy,” Accident Analysis & Prevention , vol. 205, p. 107667, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
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D. Chen, M. Zhu, H. Yang, X. Wang, and Y. Wang, “Data-driven traffic simulation: A comprehensive review,” IEEE Transactions on Intelligent Vehicles , 2024
2024
Cited alongside, same era.
H. X. Liu and S. Feng, “Curse of rarity for autonomous vehicles,” nature communications , vol. 15, no. 1, p. 4808, 2024
2024
Cited alongside, same era.
Y. Mei, T. Nie, J. Sun, and Y. Tian, “Bayesian fault injection safety testing for highly automated vehicles with uncertainty,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 12, pp. 7660–7674, 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
C. Chang, S. Wang, J. Zhang, J. Ge, and L. Li, “Llmscenario: Large language model driven scenario generation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2024
2024
Cited alongside, same era.
W. Ding, Y. Cao, D. Zhao, C. Xiao, and M. Pavone, “Realgen: Retrieval augmented generation for controllable traffic scenarios,” in European Conference on Computer Vision . Springer, 2024, pp. 93–110
2024
Later among the works it cites.
B. Chen, Z. Xu, S. Kirmani, B. Ichter, D. Sadigh, L. Guibas, and F. Xia, “Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 14 455–14 465
2024
Later among the works it cites.
Y. Mei, T. Nie, J. Sun, and Y. Tian, “Llm-attacker: Enhancing closed-loop adversarial scenario generation for autonomous driving with large language models,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–9, 2025
2025
Closest in time.
T. Nie, J. Sun, and W. Ma, “Exploring the roles of large language models in reshaping transportation systems: A survey, framework, and roadmap,” Artificial Intelligence for Transportation , vol. 1, p. 100003, 2025
2025
Closest in time.
C. Xu, A. Petiushko, D. Zhao, and B. Li, “Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 8, 2025, pp. 8797–8805
2025
Closest in time.