Sensor-based road model estimation for autonomous driving

In the course of the development and integration of the autonomous driving the knowledge about the current environment and especially the road is one of the basic requirements to fulfill the automated driving task. This information is often extracted from a precise map provided in the vehicle. Therefore the road course and the individual lanes are known in advance by using the map and a suitable ego position estimation. However, in some situations such a map may be invalid and therefore unusable. This can be a driving area which was never mapped or regions where the map is outdated because the environment has changed. The following paper addresses the problem of building a road model without using a map by only fusing measurements from different sensors mounted on the ego vehicle. As sensor measurements various information like lane markings painted on the ground, the position of other cars or occupancy grids can be used. They are transformed into a grid-based model and a geometrical description is extracted out of this model by the use of a novel path-planning based method. The proposed approach was tested with a vehicle equipped with sensors and real measurement data from German highways.