  Recent advancements in statistical learning and computational abilities have
enabled autonomous vehicle technology to develop at a much faster rate. While
many of the architectures previously introduced are capable of operating under
highly dynamic environments, many of these are constrained to smaller-scale
deployments, require constant maintenance due to the associated scalability
cost with high-definition (HD) maps, and involve tedious manual labeling. As an
attempt to tackle this problem, we propose to fuse image and pre-built point
cloud map information to perform automatic and accurate labeling of static
landmarks such as roads, sidewalks, crosswalks, and lanes. The method performs
semantic segmentation on 2D images, associates the semantic labels with point
cloud maps to accurately localize them in the world, and leverages the
confusion matrix formulation to construct a probabilistic semantic map in
bird's eye view from semantic point clouds. Experiments from data collected in
an urban environment show that this model is able to predict most road features
and can be extended for automatically incorporating road features into HD maps
with potential future work directions.
