  With the development of intelligent vehicle systems, a high-precision road
map is increasingly needed in many aspects. The automatic lane lines extraction
and modeling are the most essential steps for the generation of a precise
lane-level road map. In this paper, an automatic lane-level road map generation
system is proposed. To extract the road markings on the ground, the
multi-region Otsu thresholding method is applied, which calculates the
intensity value of laser data that maximizes the variance between background
and road markings. The extracted road marking points are then projected to the
raster image and clustered using a two-stage clustering algorithm. Lane lines
are subsequently recognized from these clusters by the shape features of their
minimum bounding rectangle. To ensure the storage efficiency of the map, the
lane lines are approximated to cubic polynomial curves using a Bayesian
estimation approach. The proposed lane-level road map generation system has
been tested on urban and expressway conditions in Hefei, China. The
experimental results on the datasets show that our method can achieve excellent
extraction and clustering effect, and the fitted lines can reach a high
position accuracy with an error of less than 10 cm
