Built on

N. H. T. S. Administration et al. , “National motor vehicle crash causation survey: Report to congress,” National Highway Traffic Safety Administration Technical Report DOT HS , vol. 811, p. 059, 2008
2008
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition . IEEE, 2012, pp. 3354–3361
2012
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.
A. Amini, W. Schwarting, G. Rosman, B. Araki, S. Karaman, and D. Rus, “Variational autoencoder for end-to-end control of autonomous driving with novelty detection and training de-biasing,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 568–575
2018
Earlier work this paper cites.
Z. Liu, Z. Miao, X. Zhan, J. Wang, B. Gong, and S. X. Yu, “Large-scale long-tailed recognition in an open world,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2537–2546
2019
Earlier work this paper cites.
J.-A. Bolte, A. Bar, D. Lipinski, and T. Fingscheidt, “Towards corner case detection for autonomous driving,” in 2019 IEEE Intelligent vehicles symposium (IV) . IEEE, 2019, pp. 438–445
2019
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020
Original
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.
M. Salehi, H. Mirzaei, D. Hendrycks, Y. Li, M. H. Rohban, and M. Sabokrou, “A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges,” arXiv preprint arXiv:2110.14051 , 2021
Original
2021
Earlier work this paper cites.
D. Bogdoll, J. Breitenstein, F. Heidecker, M. Bieshaar, B. Sick, T. Fingscheidt, and M. Zöllner, “Description of corner cases in automated driving: Goals and challenges,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1023–1028
2021
Earlier work this paper cites.
A. Jain, L. Del Pero, H. Grimmett, and P. Ondruska, “Autonomy 2.0: Why is self-driving always 5 years away?” arXiv preprint arXiv:2107.08142 , 2021
Original
2021
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.
O. Makansi, Ö. Cicek, Y. Marrakchi, and T. Brox, “On exposing the challenging long tail in future prediction of traffic actors,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 147–13 157
2021
Earlier work this paper cites.
J. Nitsch, M. Itkina, R. Senanayake, J. Nieto, M. Schmidt, R. Siegwart, M. J. Kochenderfer, and C. Cadena, “Out-of-distribution detection for automotive perception,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 2938–2943
2021
Earlier work this paper cites.
J. Mao, M. Niu, C. Jiang, H. Liang, J. Chen, X. Liang, Y. Li, C. Ye, W. Zhang, Z. Li, et al. , “One million scenes for autonomous driving: Once dataset,” arXiv preprint arXiv:2106.11037 , 2021
Original
2021
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
E. Perez, S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving, “Red teaming language models with language models,” arXiv preprint arXiv:2202.03286 , 2022
Original
2022
Cited alongside, same era.
K. Li, K. Chen, H. Wang, L. Hong, C. Ye, J. Han, Y. Chen, W. Zhang, C. Xu, D.-Y. Yeung, et al. , “Coda: A real-world road corner case dataset for object detection in autonomous driving,” in European Conference on Computer Vision . Springer, 2022, pp. 406–423
2022
Cited alongside, same era.
J. Zhou and J. Beyerer, “Corner cases in data-driven automated driving: Definitions, properties and solutions,” in 2023 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2023, pp. 1–8
2023

Then

OpenAI, “Hello gpt-4o,” https://openai.com/index/hello-gpt-4o/
2024
Closest in time.
Z.-W. Hong, I. Shenfeld, T.-H. Wang, Y.-S. Chuang, A. Pareja, J. Glass, A. Srivastava, and P. Agrawal, “Curiosity-driven red-teaming for large language models,” arXiv preprint arXiv:2402.19464 , 2024
Original
2024
Closest in time.
B. Stoler, I. Navarro, M. Jana, S. Hwang, J. Francis, and J. Oh, “Safeshift: Safety-informed distribution shifts for robust trajectory prediction in autonomous driving,” in 2024 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2024, pp. 1179–1186
2024
Closest in time.
J. Li, J. Li, S. Bae, and D. Isele, “Adaptive prediction ensemble: Improving out-of-distribution generalization of motion forecasting,” arXiv preprint arXiv:2407.09475 , 2024
Original
2024
Closest in time.
F. Arasteh, M. Elmahgiubi, B. Khamidehi, H. Mirkhani, W. Zhang, and K. Rezaee, “Learning from mistakes: a weakly-supervised method for mitigating the distribution shift in autonomous vehicle planning,” arXiv preprint arXiv:2406.01544 , 2024

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2024

Generating Out-Of-Distribution Scenarios Using Language Models

Aasi, Erfan, Nguyen, Phat, Sreeram, Shiva et al.

Understand

The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ensure that these systems can navigate safely and effectively under unpredictable conditions.

Addressing Out-Of-Distribution (OOD) driving scenarios is essential for enhancing safety, as OOD scenarios help validate the reliability of the models within the vehicle's autonomy stack.
However, generating OOD scenarios is challenging due to their long-tailed distribution and rarity in urban driving dataset.
Recently, Large Language Models (LLMs) have shown promise in autonomous driving, particularly for their zero-shot generalization and common-sense reasoning capabilities.

Built on

N. H. T. S. Administration et al. , “National motor vehicle crash causation survey: Report to congress,” National Highway Traffic Safety Administration Technical Report DOT HS , vol. 811, p. 059, 2008
2008
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition . IEEE, 2012, pp. 3354–3361
2012
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.
A. Amini, W. Schwarting, G. Rosman, B. Araki, S. Karaman, and D. Rus, “Variational autoencoder for end-to-end control of autonomous driving with novelty detection and training de-biasing,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 568–575
2018
Earlier work this paper cites.
Z. Liu, Z. Miao, X. Zhan, J. Wang, B. Gong, and S. X. Yu, “Large-scale long-tailed recognition in an open world,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2537–2546
2019
Earlier work this paper cites.
J.-A. Bolte, A. Bar, D. Lipinski, and T. Fingscheidt, “Towards corner case detection for autonomous driving,” in 2019 IEEE Intelligent vehicles symposium (IV) . IEEE, 2019, pp. 438–445
2019
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020
Original
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.
M. Salehi, H. Mirzaei, D. Hendrycks, Y. Li, M. H. Rohban, and M. Sabokrou, “A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges,” arXiv preprint arXiv:2110.14051 , 2021
Original
2021
Earlier work this paper cites.
D. Bogdoll, J. Breitenstein, F. Heidecker, M. Bieshaar, B. Sick, T. Fingscheidt, and M. Zöllner, “Description of corner cases in automated driving: Goals and challenges,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1023–1028
2021
Earlier work this paper cites.
A. Jain, L. Del Pero, H. Grimmett, and P. Ondruska, “Autonomy 2.0: Why is self-driving always 5 years away?” arXiv preprint arXiv:2107.08142 , 2021
Original
2021
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.
O. Makansi, Ö. Cicek, Y. Marrakchi, and T. Brox, “On exposing the challenging long tail in future prediction of traffic actors,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 147–13 157
2021
Earlier work this paper cites.
J. Nitsch, M. Itkina, R. Senanayake, J. Nieto, M. Schmidt, R. Siegwart, M. J. Kochenderfer, and C. Cadena, “Out-of-distribution detection for automotive perception,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 2938–2943
2021
Earlier work this paper cites.
J. Mao, M. Niu, C. Jiang, H. Liang, J. Chen, X. Liang, Y. Li, C. Ye, W. Zhang, Z. Li, et al. , “One million scenes for autonomous driving: Once dataset,” arXiv preprint arXiv:2106.11037 , 2021
Original
2021
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
E. Perez, S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving, “Red teaming language models with language models,” arXiv preprint arXiv:2202.03286 , 2022
Original
2022
Cited alongside, same era.
K. Li, K. Chen, H. Wang, L. Hong, C. Ye, J. Han, Y. Chen, W. Zhang, C. Xu, D.-Y. Yeung, et al. , “Coda: A real-world road corner case dataset for object detection in autonomous driving,” in European Conference on Computer Vision . Springer, 2022, pp. 406–423
2022
Cited alongside, same era.
J. Zhou and J. Beyerer, “Corner cases in data-driven automated driving: Definitions, properties and solutions,” in 2023 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2023, pp. 1–8
2023

Then

OpenAI, “Hello gpt-4o,” https://openai.com/index/hello-gpt-4o/
2024
Closest in time.
Z.-W. Hong, I. Shenfeld, T.-H. Wang, Y.-S. Chuang, A. Pareja, J. Glass, A. Srivastava, and P. Agrawal, “Curiosity-driven red-teaming for large language models,” arXiv preprint arXiv:2402.19464 , 2024
Original
2024
Closest in time.
B. Stoler, I. Navarro, M. Jana, S. Hwang, J. Francis, and J. Oh, “Safeshift: Safety-informed distribution shifts for robust trajectory prediction in autonomous driving,” in 2024 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2024, pp. 1179–1186
2024
Closest in time.
J. Li, J. Li, S. Bae, and D. Isele, “Adaptive prediction ensemble: Improving out-of-distribution generalization of motion forecasting,” arXiv preprint arXiv:2407.09475 , 2024
Original
2024
Closest in time.
F. Arasteh, M. Elmahgiubi, B. Khamidehi, H. Mirkhani, W. Zhang, and K. Rezaee, “Learning from mistakes: a weakly-supervised method for mitigating the distribution shift in autonomous vehicle planning,” arXiv preprint arXiv:2406.01544 , 2024

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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.

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,” arXiv preprint arXiv:2309.16292 , 2023

Original

2023

Cited alongside, same era.

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,” arXiv preprint arXiv:2312.14150 , 2023

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2023

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W. Wang, J. Xie, C. Hu, H. Zou, J. Fan, W. Tong, Y. Wen, S. Wu, H. Deng, Z. Li, et al. , “Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving,” arXiv preprint arXiv:2312.09245 , 2023

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2023

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A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. Nesnas, and M. Pavone, “Semantic anomaly detection with large language models,” Autonomous Robots , vol. 47, no. 8, pp. 1035–1055, 2023

2023

Cited alongside, same era.

H. Shao, Y. Hu, L. Wang, G. Song, S. L. Waslander, Y. Liu, and H. Li, “Lmdrive: Closed-loop end-to-end driving with large language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 120–15 130

2024

Cited alongside, same era.

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 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 14 093–14 100

2024

Cited alongside, same era.

T.-H. Wang, A. Maalouf, W. Xiao, Y. Ban, A. Amini, G. Rosman, S. Karaman, and D. Rus, “Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6687–6694

2024

Cited alongside, same era.

Original

2024

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G. Kaljavesi, X. Su, and F. Diermeyer, “Integrating end-to-end and modular driving approaches for online corner case detection in autonomous driving,” arXiv preprint arXiv:2409.01178 , 2024

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2024

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D. Bogdoll, J. Imhof, T. Joseph, and J. M. Zöllner, “Hybrid video anomaly detection for anomalous scenarios in autonomous driving,” arXiv preprint arXiv:2406.06423 , 2024

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2024

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D. Fu, X. Li, L. Wen, M. Dou, P. Cai, B. Shi, and Y. Qiao, “Drive like a human: Rethinking autonomous driving with large language models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 910–919

2024

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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

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P. Nguyen, T.-H. Wang, Z.-W. Hong, S. Karaman, and D. Rus, “Text-to-drive: Diverse driving behavior synthesis via large language models,” arXiv preprint arXiv:2406.04300 , 2024

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2024

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Q. Lu, X. Wang, Y. Jiang, G. Zhao, M. Ma, and S. Feng, “Multimodal large language model driven scenario testing for autonomous vehicles,” arXiv preprint arXiv:2409.06450 , 2024

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2024

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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, 2024

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Anthropic, “Claude 3.5 sonnet,” https://www.anthropic.com/news/claude-3-5-sonnet

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S. Sreeram, T.-H. Wang, A. Maalouf, G. Rosman, S. Karaman, and D. Rus, “Probing multimodal llms as world models for driving,” arXiv preprint arXiv:2405.05956 , 2024

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2024

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