Autonomous driving technology is now evolving at an unprecedented speed. HD maps, which are embedded with highly precise and detailed road spatial and object information, play an important role in supporting autonomous vehicles. This thesis presents the development of a semi-automated HD map creation and updating method that is capable of extracting basic road feature information to HD maps by employing raw MLS point cloud data.

The proposed HD map creation method consists of four steps: Road edge extraction, road surface extraction, road marking extraction and driving line generation. First, an existing curb-based road edge detection method is applied to extract road edge candidate points according to the elevation difference and slope between points. This thesis develops an edge vectorization algorithm based on the point's distance-to-trajectory. Then, the road surface is extracted by filtering the points inside fitted edges on the XY plane within a range of the ground elevation. In the next step, instead of using intensity to detect road markings used by most studies, this thesis fuses point clouds and images to assign each point with an RGB value to segment marking points. Marking objects are extracted by conditional Euclidean clustering and classified according to a manually defined decision tree. Finally, driving lines are generated based on the vectorized road edge and lane markings. The HD map update method varies depending on which data source is updated for the road segments, including updating images only, updating point clouds only and updating both images and point clouds.

The method is evaluated by six point clouds and image datasets collected from different types of roads. The extracted road edges are assessed by both length- and buffer-based assessment methods. The results indicate that the road edge extraction algorithm performs well in all three dimensions. The road surface extraction results confirm the high accuracy of extracted edges. In addition, the quantitative evaluations of road markings demonstrate that the proposed road marking extraction method achieves an average recall, precision, and F1-score of 94.50%, 81.65% and 87.09%.