A Digital Twin is a digital replica of a living or non-living physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a Mobility Digital Twin (MDT) framework is developed, which is defined as an Artificial Intelligence (AI)-based data-driven cloud-edgedevice framework for mobility services. This MDT consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. An example cloud-edge architecture is built with AmazonWeb Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. The effectiveness of the MDT framework is shown through the case study of three digital building blocks with their key micro-services: the Human Digital Twin with user management and driver type classification, the Vehicle Digital Twin with cloud-based Advanced Driver-Assistance Systems (ADAS), and the Traffic Digital Twin with traffic flow monitoring and variable speed limit.

With the rapid development of intelligent vehicles and Advanced Driver-Assistance Systems (ADAS), a new trend is that mixed levels of human driver engagements will be involved in the transportation system. Therefore, necessary visual guidance for drivers is vitally important under this situation to prevent potential risks. To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions. Target vehicle bounding box is drawn and matched with the help of the object detector (running on the ego-vehicle) and position information (received from the cloud). The best matching result, a 79.2% accuracy under 0.7 intersection over union threshold, is obtained with depth images served as an additional feature source. A case study on lane change prediction is conducted to show the effectiveness of the proposed data fusion methodology. In the case study, a multi-layer perceptron algorithm is proposed with modified lane change prediction approaches. Human-in-the-loop simulation results obtained from the Unity game engine reveal that the proposed model can improve highway driving performance significantly in terms of safety, comfort, and environmental sustainability.

A Digital Twin is defined as a digital replica of a real entity in the physical world. In this study, the Digital Twin simulation is developed for connected and automated vehicles (CAVs) by leveraging the Unity game engine. A Digital Twin simulation architecture is proposed, which contains the physical world and the digital world. Particularly, the digital world consists of three layers, where the Unity game objects are built to simulate the “hardware”, the Unity scripting API are used to simulate the “software”, and external tools (e.g., SUMO, MATLAB, python, and/or AWS) are leveraged to enhance the simulation functionalities. A case study of personazlied adaptive cruise control (P-ACC) is conducted to showcase the effectiveness of the proposed Digital Twin simulation, where the ACC system can be designed to satisfy each driver’s preference with the help of cloud computing.