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
It is shown that standard single-layer defenses each fail on their own and can even backfire, and called on the community to move beyond per-model alignment and toward composite safety mechanisms before multi-agent LLM systems are deployed at scale.
Zong-Hao Ying, Jia-Qi Yan, Hui-Ze Luo et al.· 0 citations
Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resulting in biased behavior when processing portraits from different social groups. Existing debiasing approaches typically compare token probabili...
Yisong Xiao, Aishan Liu, Yongxin Huang et al.· 0 citations
Conditioned on front-view visual observations, E2E-CDiff jointly denoises future motion states and executable low-level controls for route-interacting background vehicles, and mitigates the planning-control mismatch in conventional two-stage trajectory-then-controller pipelines.
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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