  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.
