LEARNING METHOD AND LEARNING DEVICE FOR INTEGRATING AN IMAGE ACQUIRED BY A CAMERA AND A POINT-CLOUD MAP ACQUIRED BY RADAR OR LIDAR IN A NEURAL NETWORK AND TESTING METHOD AND TESTING DEVICE USING THE SAME

摘要

A method for integrating, at each convolution stage in a neural network, an image generated by a camera and its corresponding point-cloud map generated by a radar, a LiDAR, or a heterogeneous sensor fusion is provided to be used for an HD map update. The method includes steps of: a computing device instructing an initial operation layer to integrate the image and its corresponding original point-cloud map, to generate a first fused feature map and a first fused point-cloud map; instructing a transformation layer to apply a first transformation operation to the first fused feature map, and to apply a second transformation operation to the first fused point-cloud map; and instructing an integration layer to integrate feature maps outputted from the transformation layer, to generate a second fused point-cloud map. By the method, an object detection and a segmentation can be performed more efficiently with a distance estimation.