Multimodal 3D object detection is a cornerstone of autonomous driving perception, yet its adversarial robustness remains poorly understood. Due to the lack of a unified cross-modal representation, existing adversarial attacks manipulate image and point cloud inputs separately, leading to limited physical realizability...
Zheng-Quan Sun, Jin Hu, Tian-Yuan Zhang et al.· 0 citations
Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception tasks and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluati...
Jingzheng Li, Xiang-Long Liu, Shikui Wei et al.· IEEE Transactions on Image P...· 3 citations
The CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs is presented, providing a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.
Tian-Yuan Zhang, Zonglei Jing, Jiangfan Liu et al.· arXiv.org· 0 citations
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