Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles
This work addresses driving safety for intelligent vehicles with mixed human driver engagement, though it appears incremental as it combines existing object detection with cloud data.
The paper tackles the problem of visual guidance for intelligent vehicles by introducing a sensor fusion methodology that integrates camera images with cloud Digital Twin information, achieving 79.2% accuracy in target vehicle bounding box matching under a 0.7 IoU threshold.
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.