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Evaluating the performance of autonomous vehicle planning algorithms necessitates simulating long-tail safety-critical traffic scenarios.
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Wang, J., Pun, A., Tu, J., Manivasagam, S., Sadat, A., Casas, S., Ren, M., Urtasun, R.: Advsim: Generating safety-critical scenarios for self-driving vehicles. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9909–9918 (2021)
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Cao, Y., Xiao, C., Anandkumar, A., Xu, D., Pavone, M.: Advdo: Realistic adversarial attacks for trajectory prediction. In: European Conference on Computer Vision. pp. 36–52. Springer (2022)
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Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
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Rempe, D., Philion, J., Guibas, L.J., Fidler, S., Litany, O.: Generating useful accident-prone driving scenarios via a learned traffic prior. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17305–17315 (2022)
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Zhang, Q., Hu, S., Sun, J., Chen, Q.A., Mao, Z.M.: On adversarial robustness of trajectory prediction for autonomous vehicles. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15159–15168 (2022)
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Dauner, D., Hallgarten, M., Geiger, A., Chitta, K.: Parting with misconceptions about learning-based vehicle motion planning. In: CoRL (2023)
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Ding, W., Xu, C., Arief, M., Lin, H., Li, B., Zhao, D.: A survey on safety-critical driving scenario generation—a methodological perspective. IEEE Transactions on Intelligent Transportation Systems (2023)
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Lu, J., Wong, K., Zhang, C., Suo, S., Urtasun, R.: Scenecontrol: Diffusion for controllable traffic scene generation. In: IEEE International Conference on Robotics and Automation (ICRA) (2024)
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Philion, J., Peng, X.B., Fidler, S.: Trajeglish: Traffic modeling as next-token prediction. In: The Twelfth International Conference on Learning Representations (2024)
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