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Recent advances in deep learning have markedly improved autonomous driving (AD) models, particularly end-to-end systems that integrate perception, prediction, and planning stages, achieving state-of-the-art performance.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in ICCV , 2015
2015
Earlier work this paper cites.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in IEEE S&P , 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,” in ACM AISec Workshop , 2017
2017
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” arXiv preprint arXiv:1706.03762 , 2017
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in CoRL , 2017
2017
Earlier work this paper cites.
W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in CVPR , 2018
2018
Earlier work this paper cites.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust physical-world attacks on deep learning visual classification,” in CVPR , 2018
2018
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in Artificial intelligence safety and security , 2018
2018
Earlier work this paper cites.
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in CVPR , 2018
2018
Earlier work this paper cites.
S. Sankaranarayanan, A. Jain, R. Chellappa, and S. N. Lim, “Regularizing deep networks using efficient layerwise adversarial training,” in AAAI , 2018
2018
Earlier work this paper cites.
M. Guo, A. Haque, D.-A. Huang, S. Yeung, and L. Fei-Fei, “Dynamic task prioritization for multitask learning,” in ECCV , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
L. Claussmann, M. Revilloud, D. Gruyer, and S. Glaser, “A review of motion planning for highway autonomous driving,” IEEE TITS , 2019
2019
Earlier work this paper cites.
H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric, “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in ICRA , 2019
2019
Earlier work this paper cites.
A. Liu, X. Liu, J. Fan, Y. Ma, A. Zhang, H. Xie, and D. Tao, “Perceptual-sensitive gan for generating adversarial patches,” in AAAI , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
F. Tramer and D. Boneh, “Adversarial training and robustness for multiple perturbations,” in NeurIPS , 2019
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
G. Velasco-Hernandez, J. Barry, J. Walsh et al. , “Autonomous driving architectures, perception and data fusion: A review,” in ICCP , 2020
2020
Cited alongside, same era.
H. Song, W. Ding, Y. Chen, S. Shen, M. Y. Wang, and Q. Chen, “Pip: Planning-informed trajectory prediction for autonomous driving,” in ECCV , 2020
2020
Cited alongside, same era.
M. Liang, B. Yang, W. Zeng, Y. Chen, R. Hu, S. Casas, and R. Urtasun, “Pnpnet: End-to-end perception and prediction with tracking in the loop,” in CVPR , 2020
2020
Cited alongside, same era.
A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun, “Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,” in ECCV , 2020
S. Wang, T. Wu, A. Chakrabarti, and Y. Vorobeychik, “Adversarial robustness of deep sensor fusion models,” in WACV , 2022
2022
Later among the works it cites.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang et al. , “Planning-oriented autonomous driving,” in CVPR , 2023
2023
Later among the works it cites.
B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q. Zhang, W. Liu, C. Huang, and X. Wang, “Vad: Vectorized scene representation for efficient autonomous driving,” in ICCV , 2023
2023
Later among the works it cites.
A. Liu, S. Tang, S. Liang, R. Gong, B. Wu, X. Liu, and D. Tao, “Exploring the relationship between architectural design and adversarially robust generalization,” in CVPR , 2023
2023
Later among the works it cites.
J. Guo, W. Bao, J. Wang, Y. Ma, X. Gao, G. Xiao, A. Liu, J. Dong, X. Liu, and W. Wu, “A comprehensive evaluation framework for deep model robustness,” Pattern Recognition , 2023
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2020
Cited alongside, same era.
A. Liu, J. Wang, X. Liu, B. Cao, C. Zhang, and H. Yu, “Bias-based universal adversarial patch attack for automatic check-out,” in ECCV , 2020
2020
Cited alongside, same era.
A. Liu, T. Huang, X. Liu, Y. Xu, Y. Ma, X. Chen, S. J. Maybank, and D. Tao, “Spatiotemporal attacks for embodied agents,” in ECCV , 2020
2020
Cited alongside, same era.
L. Huang, C. Gao, Y. Zhou, C. Xie, A. L. Yuille, C. Zou, and N. Liu, “Universal physical camouflage attacks on object detectors,” in CVPR , 2020
2020
Cited alongside, same era.
F. Croce and M. Hein, “Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,” in ICML , 2020
2020
Cited alongside, same era.
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 CVPR , 2020
2020
Cited alongside, same era.
P. Maini, E. Wong, and Z. Kolter, “Adversarial robustness against the union of multiple perturbation models,” in ICML , 2020
2020
Cited alongside, same era.
J. Wang, A. Liu, Z. Yin, S. Liu, S. Tang, and X. Liu, “Dual attention suppression attack: Generate adversarial camouflage in physical world,” in CVPR , 2021
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Wu, S. Yunas, S. Rowlands, W. Ruan, and J. Wahlström, “Adversarial driving: Attacking end-to-end autonomous driving,” in IC , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Li, S. Zhang, G. Chen, D. Wang, P. Feng, J. Wang, A. Liu, X. Yi, and X. Liu, “Towards benchmarking and assessing visual naturalness of physical world adversarial attacks,” in CVPR , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Jiang, T. Zhang, S. Liu, W. Ji, Z. Zhang, and G. Xiao, “Exploring the physical-world adversarial robustness of vehicle detection,” Electronics , 2023
2023
Later among the works it cites.
A. Liu, S. Tang, X. Chen, L. Huang, H. Qin, X. Liu, and D. Tao, “Towards defending multiple lp-norm bounded adversarial perturbations via gated batch normalization,” IJCV , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Li, Q. Fang, J. Bai, S. Chen, F. Juefei-Xu, and C. Feng, “Among us: Adversarially robust collaborative perception by consensus,” in ICCV , 2023
2023
Later among the works it cites.
T. Zhang, L. Wang, H. Li, Y. Xiao, S. Liang, A. Liu, X. Liu, and D. Tao, “Lanevil: Benchmarking the robustness of lane detection to environmental illusions,” in ACM MM , 2024
2024
Closest in time.
X. Zhang, A. Liu, T. Zhang, S. Liang, and X. Liu, “Towards robust physical-world backdoor attacks on lane detection,” in ACM MM , 2024
2024
Closest in time.
W. Jiang, L. Wang, T. Zhang, Y. Chen, J. Dong, W. Bao, Z. Zhang, and Q. Fu, “Robuste2e: Exploring the robustness of end-to-end autonomous driving,” Electronics , 2024
2024
Closest in time.
L. Wang, T. Zhang, Y. Han, M. Fang, T. Jin, and J. Kang, “Attack end-to-end autonomous driving through module-wise noise,” in CVPR Workshop , 2024
2024
Closest in time.
Y. Zhang, J. Hou, and Y. Yuan, “A comprehensive study of the robustness for lidar-based 3d object detectors against adversarial attacks,” IJCV , 2024
2024
Closest in time.
H. Wang, K. Dong, Z. Zhu, H. Qin, A. Liu, X. Fang, J. Wang, and X. Liu, “Transferable multimodal attack on vision-language pre-training models,” in IEEE S&P , 2024
2024
Closest in time.
X. Zhang, T. Zhang, Y. Zhang, and S. Liu, “Enhancing the transferability of adversarial attacks with stealth preservation,” in CVPR , 2024
2024
Closest in time.