Stereo cameras build up a complete 3D image of the car’s periphery and identify features that will help in orientation – corners of buildings, for example, lampposts and road signs. Lane markings, directional arrows, pedestrian crossings, stop lines and kerbs are also detected. Together with the relevant positional data from the GPS a detailed representation of the route is created. This provides the basis for the autonomous drive and is always open for updates. The next time the car drives along that road, the map will automatically be improved.
"Valid base data is created the first time that a route is driven. If, say, two more trips are taken along the same route, the quality of the map will be brought up to a very high level," explains Christoph Keller, who also works on vehicle tracking in Daimler’s advance development unit. Another advantage is that the map will grow as time goes on – whenever the car drives along a road it has never been on before it will create the relevant map data.
What sounds relatively straightforward actually poses numerous technical challenges. "After all, you won’t just get a good map simply because you have a lot of information.The data has to be processed in the right way in order to produce the level of detail that’s required," explains Keller. Individual algorithms, executed by computer software, produced the desired result: a car programmed to learn by itself is now able to create its own high-precision digital map. This is important for autonomous driving but also for optimising routine trips from within the data-gathering car, which is seen as an additional convenience feature.