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Ensuring safety in autonomous driving systems remains a critical challenge, particularly in handling rare but potentially catastrophic safety-critical scenarios.
Sqil: Imitation learning via reinforcement learning with sparse rewards
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Curriculum learning, in: Proceedings of the 26th annual international conference on machine learning, pp. 41–48
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Generative adversarial imitation learning
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Addressing function approximation error in actor-critic methods, in: International conference on machine learning, PMLR. pp. 1587–1596
Fujimoto, S., Hoof, H., Meger, D., 2018 · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, in: International Conference on Machine Learning, PMLR. pp. 1861–1870
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S., 2018 · 2018
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Safe reinforcement learning on autonomous vehicles, in: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. pp. 1–6
Isele, D., Nakhaei, A., Fujimura, K., 2018 · 2018
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Automatically generated curriculum based reinforcement learning for autonomous vehicles in urban environment, in: 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE. pp. 1233–1238
Qiao, Z., Muelling, K., Dolan, J.M., Palanisamy, P., Mudalige, P., 2018 · 2018
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Reinforcement learning: An introduction
Sutton, R.S., Barto, A.G., 2018 · 2018
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A framework for automated driving system testable cases and scenarios
Thorn, E., Kimmel, S.C., Chaka, M., Hamilton, B.A., et al., 2018 · 2018
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Learning to collide: An adaptive safety-critical scenarios generating method, in: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. pp. 2243–2250
Ding, W., Chen, B., Xu, M., Zhao, D., 2020 · 2020
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Responsive safety in reinforcement learning by pid lagrangian methods, in: International Conference on Machine Learning, PMLR. pp. 9133–9143
Stooke, A., Achiam, J., Abbeel, P., 2020 · 2020
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Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
Zhu, M., Wang, Y., Pu, Z., Hu, J., Wang, X., Ke, R., 2020 · 2020
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A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to ai-guided driving policy learning
Di, X., Shi, R., 2021 · 2021
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Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9710–9719
Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C.R., Zhou, Y., et al., 2021 · 2021
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Densetnt: End-to-end trajectory prediction from dense goal sets, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15303–15312
Gu, J., Sun, C., Zhao, H., 2021 · 2021
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Investigating value of curriculum reinforcement learning in autonomous driving under diverse road and weather conditions, in: 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), IEEE. pp. 358–363
Ozturk, A., Gunel, M.B., Dagdanov, R., Vural, M.E., Yurdakul, F., Dal, M., Ure, N.K., 2021 · 2021
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Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain
Scanlon, J.M., Kusano, K.D., Daniel, T., Alderson, C., Ogle, A., Victor, T., 2021 · 2021
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Scenegen: Learning to generate realistic traffic scenes, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 892–901
Tan, S., Wong, K., Wang, S., Manivasagam, S., Ren, M., Urtasun, R., 2021 · 2021
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A survey on curriculum learning
Wang, X., Chen, Y., Zhu, W., 2021 · 2021
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An end-to-end curriculum learning approach for autonomous driving scenarios
Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M., 2022 · 2022
A survey on multimodal large language models for autonomous driving, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 958–979
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.D., et al., 2024 · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al., 2024 · 2024
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Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
He, X., Huang, W., Lv, C., 2024 · 2024
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Comparison of waymo rider-only crash data to human benchmarks at 7.1 million miles
Kusano, K.D., Scanlon, J.M., Chen, Y.H., McMurry, T.L., Chen, R., Gode, T., Victor, T., 2024 · 2024
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Visual instruction tuning
Liu, H., Li, C., Wu, Q., Lee, Y.J., 2024 · 2024
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Cited alongside, same era.
Trustworthy safety improvement for autonomous driving using reinforcement learning
Cao, Z., Xu, S., Jiao, X., Peng, H., Yang, D., 2022 · 2022
Cited alongside, same era.
imitation: Clean imitation learning implementations
Gleave, A., Taufeeque, M., Rocamonde, J., Jenner, E., Wang, S.H., Toyer, S., Ernestus, M., Belrose, N., Emmons, S., Russell, S., 2022 · 2022
Cited alongside, same era.
State dropout-based curriculum reinforcement learning for self-driving at unsignalized intersections, in: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. pp. 12219–12224
Khaitan, S., Dolan, J.M., 2022 · 2022
Cited alongside, same era.
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
Rempe, D., Philion, J., Guibas, L.J., Fidler, S., Litany, O., 2022 · 2022
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M2i: From factored marginal trajectory prediction to interactive prediction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6543–6552
Sun, Q., Huang, X., Gu, J., Williams, B.C., Zhao, H., 2022 · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al., 2023 · 2023
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A survey on safety-critical driving scenario generation—a methodological perspective
Ding, W., Xu, C., Arief, M., Lin, H., Li, B., Zhao, D., 2023 · 2023
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Scenecontrol: Diffusion for controllable traffic scene generation, in: 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 16908–16914
Lu, J., Wong, K., Zhang, C., Suo, S., Urtasun, R., 2024 · 2024
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Evolving testing scenario generation and intelligence evaluation for automated vehicles
Ma, Y., Jiang, W., Zhang, L., Chen, J., Wang, H., Lv, C., Wang, X., Xiong, L., 2024 · 2024
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Continuously learning, adapting, and improving: A dual-process approach to autonomous driving, in: The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS)
Mei, J., Ma, Y., Yang, X., Wen, L., Cai, X., Li, X., Fu, D., Zhang, B., Cai, P., Dou, M., et al., 2024 · 2024
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Continual driving policy optimization with closed-loop individualized curricula, in: 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 6850–6857
Niu, H., Xu, Y., Jiang, X., Hu, J., 2024 · 2024
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Peng, Z., Zhou, X., Zheng, L., Wang, Y., Ma, J., 2024 · 2024
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Ruan, B.K., Tsui, H.T., Li, Y.H., Shuai, H.H., 2024 · 2024
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Scvlm: a vision-language model for driving safety critical event understanding
Shi, L., Jiang, B., Guo, F., 2024 · 2024
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Drivelm: Driving with graph visual question answering, in: European Conference on Computer Vision
Sima, C., Renz, K., Chitta, K., Chen, L., Zhang, H., Xie, C., Beißwenger, J., Luo, P., Geiger, A., Li, H., 2024 · 2024
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Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution
Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., Ge, W., et al., 2024 · 2024
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Dilu: A knowledge-driven approach to autonomous driving with large language models, in: International Conference on Learning Representations
Wen, L., Fu, D., Li, X., Cai, X., Ma, T., Cai, P., Dou, M., Shi, B., He, L., Qiao, Y., 2024 · 2024
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Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Wu, J., Huang, C., Huang, H., Lv, C., Wang, Y., Wang, F.Y., 2024 · 2024
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Hazardvlm: A video language model for real-time hazard description in automated driving systems
Xiao, D., Dianati, M., Jennings, P., Woodman, R., 2024 · 2024
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Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15459–15469
Zhang, J., Xu, C., Li, B., 2024 · 2024
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