  Lane detection algorithms are crucial for the development of autonomous
vehicles technologies. The more extended approach is to use cameras as sensors.
However, LIDAR sensors can cope with weather and light conditions that cameras
can not. In this paper, we introduce a method to extract road markings from the
reflectivity data of a 64-layers LIDAR sensor. First, a plane segmentation
method along with region grow clustering was used to extract the road plane.
Then we applied an adaptive thresholding based on Otsu s method and finally, we
fitted line models to filter out the remaining outliers. The algorithm was
tested on a test track at 60km/h and a highway at 100km/h. Results showed the
algorithm was reliable and precise. There was a clear improvement when using
reflectivity data in comparison to the use of the raw intensity data both of
them provided by the LIDAR sensor.
