3 citations · 5 across the 11 of their papers we have counts for
13 papers
Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An -Potential Game Framework
Anran Hu, Zhexin Wang, Yufei Zhang +1
We study noncooperative multi-vehicle games among heterogeneous autonomous vehicles, where each vehicle adopts a decentralized closed-loop policy based on its own state, and optimi…
-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…
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…
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…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
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…
SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets
Zhaobin Mo, Yunlong Li, Xuan Di
Safety-critical driving data is crucial for developing safe and trustworthy self-driving algorithms. Due to the scarcity of safety-critical data in naturalistic datasets, current a…
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…
diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs
Zhaobin Mo, Haotian Xiang, Xuan Di
Spatiotemporal prediction over graphs (STPG) is challenging, because real-world data suffers from the Out-of-Distribution (OOD) generalization problem, where test data follow diffe…
Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs
Zhaobin Mo, Qingyuan Liu, Baohua Yan +2
Spatiotemporal prediction over graphs (STPG) is crucial for transportation systems. In existing STPG models, an adjacency matrix is an important component that captures the relatio…
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…