- global planning (routing)
- local planning (associate lanes with traffic lights and signs)
- semantic knowledge that can be used for behavior planning and prediction estimates
- use in the localization task. A very good way of summarizing the benefits of the HD map is to treat it as an additional sensor that extends the viewing horizon for the car.
Collecting data for HD maps and keeping them up to date is costly, therefore machine learning and automation is used in order to make these processes scalable for large areas. We employ deep learning to generate HD maps from different data sources: orthophotos, lidar point clouds, dashcam footage, etc. We also aim for a unified representation of the map data that could be later converted into any standardized HD map format, e.g. OpenDRIVE, Lanelet2, Autoware CSV.