Hi all: I'm thinking to build the lane model for OSM using machine learning techniques. How can I get multiple gpx files from osm database for certain roads? Thanks in advance for any advice or feedback. Best D. Below are my half-baked ideas: 1) Motivation: a) According to Sebastian Thrun, in order to build the autonomous driving car, they push google map to 15cm accuracy with lane models !!! (I guess they do it in an expensive way, maybe only for part of CA to test their car) http://www.youtube.com/watch?v=bp9KBrH8H04 b) Also, several OSM members have already proposed the lane tag for the map. However, it'll be a pain for our dear brave volunteers to track and mark the lane they were in. 2) One possible way out: a) the bottleneck is that gpx files uploaded by users are often too noisy to figure out the lane assignment. But we have the "turn constraint" (change to inner/outer lane before turning left/right) and relative geometry shape of the route. b) Given multiple gpx files for one road segment, we can have a robust estimation of the lane model using machine learning techniques. c) Also, such technique may be useful for map refinement instead of asking users to correct by their own where the accuracy is in meters.

Hi all: I'm thinking to build the lane model for OSM using machine learning techniques. How can I get multiple gpx files from osm database for certain roads? Thanks in advance for any advice or feedback. Best D. Below are my half-baked ideas: 1) Motivation: a) According to Sebastian Thrun, in order to build the autonomous driving car, they push google map to 15cm accuracy with lane models !!! (I guess they do it in an expensive way, maybe only for part of CA to test their car) http://www.youtube.com/watch?v=bp9KBrH8H04 b) Also, several OSM members have already proposed the lane tag for the map. However, it'll be a pain for our dear brave volunteers to track and mark the lane they were in. 2) One possible way out: a) the bottleneck is that gpx files uploaded by users are often too noisy to figure out the lane assignment. But we have the "turn constraint" (change to inner/outer lane before turning left/right) and relative geometry shape of the route. b) Given multiple gpx files for one road segment, we can have a robust estimation of the lane model using machine learning techniques. c) Also, such technique may be useful for map refinement instead of asking users to correct by their own where the accuracy is in meters.