论文标题
智能车辆的相机和云数字双胞胎信息的传感器融合
Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles
论文作者
论文摘要
随着智能车辆和高级驾驶援助系统（ADA）的快速发展，运输系统中涉及人类驾驶员的交战水平。在这种情况下，对驾驶员的视觉指导至关重要，以防止潜在的风险。为了推动视觉引导系统的开发，我们引入了一种新型的传感器融合方法，从云中整合了相机图像和数字双知识。通过将在自我车辆上运行的对象检测器的结果和云中的位置信息结合起来，可以绘制目标车辆边界框并匹配。最佳的匹配结果在与联合（IOU）阈值相交的0.7交叉点下具有79.2％的精度，是用深度图像作为附加特征来源获得的。基于游戏发动机的仿真结果还表明，视觉引导系统可以改善驱动安全性与云数字双胞胎系统大量合作。
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.
