6 papers
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…
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,…
Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks
Zhaobin Mo, Xiangyi Liao, Dominik A. Karbowski +1
Understanding and predicting the precursors of traffic breakdowns is critical for improving road safety and traffic flow management. This paper presents a novel approach combining…
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…
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…