A road-terrain categorization method for driver assistance systems of a vehicle, wherein road-terrain is a type of surface in the vehicle's environment and includes sidewalks and traffic islands, said method comprising: a) sensing the environment of a vehicle with at least one sensor; b) transforming a sensor signal from the at least one sensor into at least one confidence map of local properties, especially physical properties, e.g. elevation, or visual properties, e.g. color, texture, of the environment by using at least one base classifier (M11-2.1; M11-2.2), wherein the at least one base classifier (M11-2.1; M11-2.2) transforms the sensor signal from the at least one sensor into at least one value-continuous spatial representation, i.e. the confidence map, representing the local properties by at least one value-continuous confidence value; c) generating spatial features reflecting the local properties and their spatial layout (M12-1.1, M12-1.2) based on the at least one confidence map, wherein the generation of a spatial feature for a specific location in the environment of the vehicle comprises extracting a ray (1,2,3,4,5,6), wherein a ray (1,2,3,4,5,6) is defined as a directed line with a certain angular orientation starting from the location in the vehicle's environment, and analyzing the confidence values of the confidence map along the ray (1,2,3,4,5,6) to extract the spatial feature, wherein the analysis of the confidence values along the ray (1,2,3,4,5,6) is performed by integrating the confidence values of the confidence map along the ray (1,2,3,4,5,6) and extracting the ray length (t1, t2), at which the integral exceeds an absorption threshold, which is a certain numeric value; and d) classifying locations in the environment of the vehicle to a certain category of road terrain (T11, T21, T22, T31, T32, NT) based on the generated spatial features. a) sensing the environment of a vehicle with at least one sensor; b) transforming a sensor signal from the at least one sensor into at least one confidence map of local properties, especially physical properties, e.g. elevation, or visual properties, e.g. color, texture, of the environment by using at least one base classifier (M11-2.1; M11-2.2), wherein the at least one base classifier (M11-2.1; M11-2.2) transforms the sensor signal from the at least one sensor into at least one value-continuous spatial representation, i.e. the confidence map, representing the local properties by at least one value-continuous confidence value; c) generating spatial features reflecting the local properties and their spatial layout (M12-1.1, M12-1.2) based on the at least one confidence map, wherein the generation of a spatial feature for a specific location in the environment of the vehicle comprises extracting a ray (1,2,3,4,5,6), wherein a ray (1,2,3,4,5,6) is defined as a directed line with a certain angular orientation starting from the location in the vehicle's environment, and analyzing the confidence values of the confidence map along the ray (1,2,3,4,5,6) to extract the spatial feature, wherein the analysis of the confidence values along the ray (1,2,3,4,5,6) is performed by integrating the confidence values of the confidence map along the ray (1,2,3,4,5,6) and extracting the ray length (t1, t2), at which the integral exceeds an absorption threshold, which is a certain numeric value; and d) classifying locations in the environment of the vehicle to a certain category of road terrain (T11, T21, T22, T31, T32, NT) based on the generated spatial features.