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Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning.
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Multivehicle cooperative driving using cooperative perception: Design and experimental validation
Seong-Woo Kim, B. Qin, Z. J. Chong, Xiaotong Shen, Wei Liu, M. Ang, Emilio Frazzoli, and D. Rus · 2015
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Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2017
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
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Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds
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Adversarial examples: Opportunities and challenges
Jiliang Zhang and Chen Li · 2019
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Adversarial sensor attack on lidar-based perception in autonomous driving
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Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
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Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems
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Fooling lidar perception via adversarial trajectory perturbation
Yiming Li, Congcong Wen, Felix Juefei-Xu, and Chen Feng · 2021
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Exploring adversarial robustness of multi-sensor perception systems in self driving
James Tu, Huichen Li, Xinchen Yan, Mengye Ren, Yun Chen, Ming Liang, Eilyan Bitar, Ersin Yumer, and Raquel Urtasun · 2021
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Vision-centric bev perception: A survey
Yuexin Ma, Tai Wang, Xuyang Bai, Huitong Yang, Yuenan Hou, Yaming Wang, Yu Qiao, Ruigang Yang, Dinesh Manocha, and Xinge Zhu · 2022
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3d semantic scene completion: A survey
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Physically realizable adversarial examples for lidar object detection
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Adversarial attacks and defenses in deep learning
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Eugene Valassakis, Kamil Dreczkowski, and Edward Johns · 2022
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Uncertainty quantification of collaborative detection for self-driving
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