69 citations · 140 across the 11 of their papers we have counts for
11 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…
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…
Can LLMs Understand Social Norms in Autonomous Driving Games?
Boxuan Wang, Haonan Duan, Yanhao Feng +4
Social norm is defined as a shared standard of acceptable behavior in a society. The emergence of social norms fosters coordination among agents without any hard-coded rules, which…
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
Yongjie Fu, Anmol Jain, Xuan Di +2
The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VL…
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model
Yongjie Fu, Yunlong Li, Xuan Di
Autonomous driving training requires a diverse range of datasets encompassing various traffic conditions, weather scenarios, and road types. Traditional data augmentation methods o…
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
Xuan Di, Rongye Shi, Zhaobin Mo +1
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL)…
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
Zhaobin Mo, Yongjie Fu, Xuan Di
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL). The uncertainty of t…
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
Zhaobin Mo, Yongjie Fu, Daran Xu +1
This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. TrafficFlowGAN…
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles
Mobin Zhao, Wangzhi Li, Yongjie Fu +2
This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called "CVLight", that leverages data collected…
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment
Zhenyu Shou, Xu Chen, Yongjie Fu +1
This paper aims to develop a paradigm that models the learning behavior of intelligent agents (including but not limited to autonomous vehicles, connected and automated vehicles, o…