AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications
Yongjie Fu, Mehmet K. Turkcan, Mahshid Ghasemi, Zhaobin Mo, Chengbo Zang, Abhishek Adhikari, Zoran Kostic, Gil Zussman, Xuan Di
TL;DR
The paper addresses how to design AI-powered digital twins for urban transportation that incorporate vulnerable road users within a cyber-physical system framework. It presents a comprehensive CPS-enabled DT pipeline, spanning sensing, perception, real-time analytics, edge-cloud computing, and communication, and discusses use-case deployments such as VRU safety warnings and adaptive signal control. The authors survey data modalities, object detection/tracking, video analytics, networking, and physical testbeds, and propose approaches for responsible AI with safety guarantees and domain knowledge. They also identify emerging trends, open challenges, and a road map for cross-disciplinary research and standardized evaluation to enable practical, scalable urban DT deployments.
Abstract
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 the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.