| dc.description.abstract | With the growing computing capacity and the development in the field of Geoinformation, governments and enterprises have actively invested in the research and development of autonomous driving systems. Most autonomous driving technologies rely on multiple on-board sensors for real-time operation. However, the implementation of pre-built High-Definition map (HD Map) can provide detail information of the surrounding environment for self-driving cars. For instance, positioning and orientation, which can significantly reduce the computational burden of on-board computers. Therefore, efficiently producing data layers for HD maps is an important step in the development of self-driving car technology. The purpose of this research is to apply the open source street scene image dataset and deep learning algorithms to extract lane markings from the Mobile Mapping System (MMS) images, and to automatically create the data layer for HD maps.
The proposed scheme consists of three parts: (1) semantic segmentation, (2) direct georeferencing, (3) lane marking correction. In the semantic segmentation part, this study uses the open source self-driving car image dataset ApolloScape to train the deep learning model. In order to increase the accuracy of image segmentation, this study crop the training images and remove the surveying car-roof area which is not shown in the test data. Also, it removes parts of training images with high illumination and noise. Afterward, in order to get the coordinates of lane markings, this study uses photogrammetry method and orientation parameters of camera to derived the positional information of the lane markings. Finally, produce the HD Map data layer according to the regulations issued by Ministry of Transportation and Communications, Taiwan.
The experimental results show that the proposed method can effectively identify the road markings in the MMS images. In addition, the developed method can further correct the mis-classified and missing parts in the subsequent lane marking correction steps, and produce reliable road marking data layer for High -Definition Map. | en_US |