8 papers
LLM-based Realistic Safety-Critical Driving Video Generation
Yongjie Fu, Ruijian Zha, Pei Tian +1
Designing diverse and safety-critical driving scenarios is essential for evaluating autonomous driving systems. In this paper, we propose a novel framework that leverages Large Lan…
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
Yongjie Fu, Lingyun Zhong, Zifan Li +1
Multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control (ATSC), enabling multiple intersections to coordinate signal timings in real time. H…
Preserving Spectral Structure and Statistics in Diffusion Models
Baohua Yan, Jennifer Kava, Qingyuan Liu +1
Standard diffusion models (DMs) rely on the total destruction of data into non-informative white noise, forcing the backward process to denoise from a fully unstructured noise stat…
AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications
Yongjie Fu, Mehmet K. Turkcan, Mahshid Ghasemi +6
We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is t…
-Potential Games for Decentralized Control of Connected and Automated Vehicles
Xuan Di, Anran Hu, Zhexin Wang +1
Designing scalable and safe control strategies for large populations of connected and automated vehicles (CAVs) requires accounting for strategic interactions among heterogeneous a…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +44
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…