A method for spatial analysis of a traffic scene, comprising acquiring a spatial semantic environment map including semantic context data, wherein the semantic context data are confidence values or binary values for a semantic label for grid cells of the spatial semantic environment map, calculating at least one feature for at least one cell on the spatial environment map taking into account the semantic context of the cell, wherein the at least one feature is a ray based feature calculated by integrating the confidence values or binary values to provide an integrated semantic label along at least one ray extending from the at least one cell in at least one of a space dimension and a time dimension, and the at least one feature corresponds to a distance at which the integrated confidence values or binary values exceed at least one predetermined threshold, determining a category for the at least one cell based on the at least one calculated feature, and generating and outputting an analysis signal comprising data about the determined category of the at least one cell for predicting a traffic scene in a driver assistance system, to an actuating means for influencing the control of an ego-vehicle and/or to means for informing a driver of the ego-vehicle, and characterized in that the method comprises a step of determining a spatial and/or temporal ray shape for the at least one ray from a plurality of predefined ray shapes based on the semantic context of the at least one cell where the at least one ray starts, and the ray starts in the at least one cell and has a shape which takes into account the content of the underlying semantic representation. acquiring a spatial semantic environment map including semantic context data, wherein the semantic context data are confidence values or binary values for a semantic label for grid cells of the spatial semantic environment map, calculating at least one feature for at least one cell on the spatial environment map taking into account the semantic context of the cell, wherein the at least one feature is a ray based feature calculated by integrating the confidence values or binary values to provide an integrated semantic label along at least one ray extending from the at least one cell in at least one of a space dimension and a time dimension, and the at least one feature corresponds to a distance at which the integrated confidence values or binary values exceed at least one predetermined threshold, determining a category for the at least one cell based on the at least one calculated feature, and generating and outputting an analysis signal comprising data about the determined category of the at least one cell for predicting a traffic scene in a driver assistance system, to an actuating means for influencing the control of an ego-vehicle and/or to means for informing a driver of the ego-vehicle, and characterized in that the method comprises a step of determining a spatial and/or temporal ray shape for the at least one ray from a plurality of predefined ray shapes based on the semantic context of the at least one cell where the at least one ray starts, and the ray starts in the at least one cell and has a shape which takes into account the content of the underlying semantic representation.