  With the rapid development of intelligent vehicles and Advanced Driving
Assistance Systems (ADAS), a mixed level of human driver engagements is
involved in the transportation system. Visual guidance for drivers is essential
under this situation to prevent potential risks. To advance the development of
visual guidance systems, we introduce a novel sensor fusion methodology,
integrating camera image and Digital Twin knowledge from the cloud. Target
vehicle bounding box is drawn and matched by combining results of object
detector running on ego vehicle and position information from the cloud. The
best matching result, with a 79.2% accuracy under 0.7 Intersection over Union
(IoU) threshold, is obtained with depth image served as an additional feature
source. Game engine-based simulation results also reveal that the visual
guidance system could improve driving safety significantly cooperate with the
cloud Digital Twin system.
