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
Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions. Existing learning-based methods often struggle to balance controllability and realism, offering either limited fine-grained control...
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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