  A Mobility Digital Twin is an emerging implementation of digital twin
technology in the transportation domain, which creates digital replicas for
various physical mobility entities, such as vehicles, drivers, and pedestrians.
Although a few work have investigated the applications of mobility digital twin
recently, the extent to which it can facilitate safer autonomous vehicles
remains insufficiently explored. In this paper, we first propose visualization
of mobility digital twin, which aims to augment the existing perception systems
in connected and autonomous vehicles through twinning high-fidelity and
manipulable geometry representations for causal traffic participants, such as
surrounding pedestrians and vehicles, in the digital space. An end-to-end
system framework, including image data crowdsourcing, preprocessing,
offloading, and edge-assisted 3D geometry reconstruction, is designed to enable
real-world development of the proposed visualization of mobility digital twin.
We implement the proposed system framework and conduct a case study to assess
the twinning fidelity and physical-to-digital synchronicity within different
image sampling scenarios and wireless network conditions. Based on the case
study, future challenges of the proposed visualization of mobility digital twin
are discussed toward the end of the paper.
