arXiv:2607. 09772v1 Announce Type: cross Abstract: Autonomous driving systems require reliable safety validation before real-world deployment.
By Yongzhi Liu
The paper outlines methods and applications for creating AI‑powered digital twins (DTs) tailored to urban traffic management. It emphasizes that while most DT research focuses on sensing and perception, the true differentiator lies in the DT’s predictive and decision‑making "brain" that extracts patterns and informs actions. By integrating artificial intelligence with low‑latency, high‑bandwidth cyber‑physical systems, the authors propose a framework that can guide researchers and practitioners in addressing challenges, fostering interdisciplinary dialogue, and unlocking diverse urban transportation applications.
By Yongjie Fu, Mehmet K. Turkcan, Mahshid Ghasemi, Zhaobin Mo, Chengbo Zang, Abhishek Adhikari, Zoran Kostic, Gil Zussman, Xuan Di
arXiv:2606. 06375v1 Announce Type: new Abstract: Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data.
By Ching Yau Fergus Mok, Lavindra de Silva, Varun Kumar Reja, Ioannis Brilakis
arXiv:2601. 11665v3 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain.
By Amir Farzin Nikkhah, Dong Chen, Bradford Campbell, Somayeh Asadi, Arsalan Heydarian
For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it.
The paper introduces RACO, a reliability‑aware adaptive coarse‑to‑fine navigation framework for inspection‑oriented UAV vision‑language navigation. It treats the coarse goal as a runtime hypothesis, using object‑level anchors to correct localization before and at the transition to the fine stage, and applies scale‑adaptive terminal refinement for near‑miss cases. RACO is evaluated on the new LG‑UVI inspection setting and outperforms the HETT baseline by 9.53 and 7.98 percentage points on validation‑unseen and test‑unseen, respectively, while improving inspection‑region arrival and reducing false verification risk.
By Sen Wang, Yiming Sun, Jiaxuan He, Pengfei Zhu