3 papers
cs.CV2026
Instance-Level Post Hoc Uncertainty Quantification in Object Detection
Chongzhe Zhang, Zifan Zeng, Qunli Zhang +2
Object detection is a safety-critical component of autonomous driving. It is essential to quantify the uncertainty in bounding-box predictions for safety assurance. Post hoc uncert…
cs.AI2025
The Safety Challenge of World Models for Embodied AI Agents: A Review
Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang +8
The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental…
cs.AI2024
World Models: The Safety Perspective
Zifan Zeng, Chongzhe Zhang, Feng Liu +4
With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially…