  Up-to-date High-Definition (HD) maps are essential for self-driving cars. To
achieve constantly updated HD maps, we present a deep neural network (DNN),
Diff-Net, to detect changes in them. Compared to traditional methods based on
object detectors, the essential design in our work is a parallel feature
difference calculation structure that infers map changes by comparing features
extracted from the camera and rasterized images. To generate these rasterized
images, we project map elements onto images in the camera view, yielding
meaningful map representations that can be consumed by a DNN accordingly. As we
formulate the change detection task as an object detection problem, we leverage
the anchor-based structure that predicts bounding boxes with different change
status categories. To the best of our knowledge, the proposed method is the
first end-to-end network that tackles the high-definition map change detection
task, yielding a single stage solution. Furthermore, rather than relying on
single frame input, we introduce a spatio-temporal fusion module that fuses
features from history frames into the current, thus improving the overall
performance. Finally, we comprehensively validate our method's effectiveness
using freshly collected datasets. Results demonstrate that our Diff-Net
achieves better performance than the baseline methods and is ready to be
integrated into a map production pipeline maintaining an up-to-date HD map.
