Lane Map Generation in Rectified Raster Maps with Past Vehicle Traces

Lane-level maps provide crucial detail to autonomous vehicle perception and decision making systems. Many common HD maps require human annotators to painstakingly label attributes such as lane geometry, connectivity, and speed limit. In this paper, we propose a method to derive these attributes automatically. Our method uses lane features and previous vehicle experience to generate potentials in a rectified raster map. We then infer the driving lanes within a Markov random field. Moreover, the lane topologies are extracted and represented by parsing the roadway into different segments based on the number of driving lanes and generating lane transitions between adjacent road segments. Lane attributes, such as speed limit and stop locations, are inferred from statistical analysis of previous vehicle experiences. Our approach is supported by experiments demonstrating effective automatic lane map generation on real-world roads.