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. 28625v1 Announce Type: cross Abstract: Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety.
By Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury
arXiv:2608. 06227v1 Announce Type: cross Abstract: Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws.
By Christo Kurisummoottil Thomas, Omar Hashash, Walid Saad
The paper introduces FedQoS, an asynchronous federated learning framework designed for multimodal in‑cabin interaction in smart vehicles. It uses a two‑phase gating mechanism: a resource‑aware training gate that starts local learning only when sensing buffers and energy reserves meet safety thresholds, and a QoS‑aware transmission policy that gates uplink updates based on an efficiency score balancing model novelty, latency, and energy costs. Experiments on vehicular datasets show FedQoS achieves competitive personalized accuracy with only marginal loss compared to FedAvg, while reducing communication overhead by 76.7% and latency cost by 26.0%.
By Baran Can G\"ul, Mert Nak{\i}p, Nasser Jazdi, Michael Weyrich
VIPS is a benchmark for vehicle‑to‑infrastructure cooperative autonomous driving that uses pseudo‑simulation to combine vehicle and infrastructure observations, enabling scalable yet realistic evaluation of robustness and error propagation without full simulation. The paper also introduces CoS‑V2X, a cooperative planning framework that employs sparse representations to model vehicle‑infrastructure interactions efficiently and robustly under heterogeneous observations.
By Hoonhee Cho, Jae-Young Kang, Giwon Lee, Hyemin Yang, Heejun Park, Kuk-Jin Yoon