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Autonomous vehicles must navigate safely in complex driving environments.
Mnist-c: A robustness benchmark for computer vision
Mu, N.; and Gilmer, J. 2019 · 1906
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Robustbench: a standardized adversarial robustness benchmark
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Deformable detr: Deformable transformers for end-to-end object detection
Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2020 · 2010
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Intriguing properties of neural networks
Szegedy, C. 2013 · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Domain-adversarial training of neural networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; March, M.; and Lempitsky, V. 2016 · 2016
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Ilg, E.; Mayer, N.; Saikia, T.; Keuper, M.; Dosovitskiy, A.; and Brox, T. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017 · 2017
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Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z.; and Vasconcelos, N. 2018 · 2018
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Liteflownet: A lightweight convolutional neural network for optical flow estimation
Hui, T.-W.; Tang, X.; and Loy, C. C. 2018 · 2018
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Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A.; and Weiss, Y. 2019 · 2019
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Learning by Cheating
Chen, D.; Zhou, B.; Koltun, V.; and Krähenbühl, P. 2019 · 2019
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An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
Lee, Y.; Hwang, J.-w.; Lee, S.; Bae, Y.; and Park, J. 2019 · 2019
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A simple way to make neural networks robust against diverse image corruptions
Rusak, E.; Schott, L.; Zimmermann, R. S.; Bitterwolf, J.; Bringmann, O.; Bethge, M.; and Brendel, W. 2020 · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Teed, Z.; and Deng, J. 2020 · 2020
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Learning to drive from a world on rails
Chen, D.; Koltun, V.; and Krähenbühl, P. 2021 · 2021
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2021 · 2021
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On interaction between augmentations and corruptions in natural corruption robustness
Mintun, E.; Kirillov, A.; and Xie, S. 2021 · 2021
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Data augmentation can improve robustness
Vad: Vectorized scene representation for efficient autonomous driving
Jiang, B.; Chen, S.; Xu, Q.; Liao, B.; Chen, J.; Zhou, H.; Zhang, Q.; Liu, W.; Huang, C.; and Wang, X. 2023 · 2023
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Drivedreamer: Towards real-world-driven world models for autonomous driving
Wang, X.; Zhu, Z.; Huang, G.; Chen, X.; Zhu, J.; and Lu, J. 2023 · 2023
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Pointodyssey: A large-scale synthetic dataset for long-term point tracking
Zheng, Y.; Harley, A. W.; Shen, B.; Wetzstein, G.; and Guibas, L. J. 2023 · 2023
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Vadv2: End-to-end vectorized autonomous driving via probabilistic planning
Chen, S.; Jiang, B.; Gao, H.; Liao, B.; Xu, Q.; Zhang, Q.; Huang, C.; Liu, W.; and Wang, X. 2024 · 2024
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NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Dauner, D.; Hallgarten, M.; Li, T.; Weng, X.; Huang, Z.; Yang, Z.; Li, H.; Gilitschenski, I.; Ivanovic, B.; Pavone, M.; Geiger, A.; and Chitta, K. 2024 · 2024
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Rebuffi, S.-A.; Gowal, S.; Calian, D. A.; Stimberg, F.; Wiles, O.; and Mann, T. A. 2021 · 2021
Cited alongside, same era.
End-to-end urban driving by imitating a reinforcement learning coach
Zhang, Z.; Liniger, A.; Dai, D.; Yu, F.; and Van Gool, L. 2021 · 2021
Cited alongside, same era.
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
Chitta, K.; Prakash, A.; Jaeger, B.; Yu, Z.; Renz, K.; and Geiger, A. 2022 · 2022
Cited alongside, same era.
3d common corruptions and data augmentation
Kar, O. F.; Yeo, T.; Atanov, A.; and Zamir, A. 2022 · 2022
Cited alongside, same era.
Benchmarking robustness of 3d point cloud recognition against common corruptions
Sun, J.; Zhang, Q.; Kailkhura, B.; Yu, Z.; Xiao, C.; and Mao, Z. M. 2022 · 2022
Cited alongside, same era.
Gmflow: Learning optical flow via global matching
Xu, H.; Zhang, J.; Cai, J.; Rezatofighi, H.; and Tao, D. 2022 · 2022
Cited alongside, same era.
Planning-oriented autonomous driving
Hu, Y.; Yang, J.; Chen, L.; Li, K.; Sima, C.; Zhu, X.; Chai, S.; Du, S.; Lin, T.; Wang, W.; et al. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
Jia, X.; Yang, Z.; Li, Q.; Zhang, Z.; and Yan, J. 2024a · 2024
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Drivelm: Driving with graph visual question answering
Sima, C.; Renz, K.; Chitta, K.; Chen, L.; Zhang, H.; Xie, C.; Beißwenger, J.; Luo, P.; Geiger, A.; and Li, H. 2024 · 2024
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Refining pre-trained motion models
Sun, X.; Harley, A. W.; and Guibas, L. J. 2024 · 2024
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Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L.; Kang, B.; Huang, Z.; Xu, X.; Feng, J.; and Zhao, H. 2024 · 2024
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Drama: An efficient end-to-end motion planner for autonomous driving with mamba
Yuan, C.; Zhang, Z.; Sun, J.; Sun, S.; Huang, Z.; Lee, C. D. W.; Li, D.; Han, Y.; Wong, A.; Tee, K. P.; et al. 2024 · 2024
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Fu, H.; Zhang, D.; Zhao, Z.; Cui, J.; Liang, D.; Zhang, C.; Zhang, D.; Xie, H.; Wang, B.; and Bai, X. 2025 · 2025
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DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
Liao, B.; Chen, S.; Yin, H.; Jiang, B.; Wang, C.; Yan, S.; Zhang, X.; Li, X.; Zhang, Y.; Zhang, Q.; and Wang, X. 2025 · 2025
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Enhancing Autonomous Driving Safety with Collision Scenario Integration
Wang, Z.; Lan, S.; Sun, X.; Chang, N.; Li, Z.; Yu, Z.; and Alvarez, J. M. 2025 · 2025
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Zhou, Z.; Cai, T.; Zhao, S. Z.; Zhang, Y.; Huang, Z.; Zhou, B.; and Ma, J. 2025 · 2025
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